Computer Science

Research area: Eric Paquette conducts research in Computer Graphics, and on the specific topics of 3D reconstruction from images, 3D Gaussian splatting (3DGS), deep neural networks applied to computer graphics, animation, geometry processing, texture synthesis, and simulation of natural phenomena.

Student roles:
The student will be involved in working together with master’s and PhD students by testing, designing, and/or implementing state-of-the-art Computer Graphics algorithms. The project will likely involve analyzing and improving current computer graphics code bases. Most of the code will be in either C++ or Python. The project might also involve some use of 3D software such as Blender or SideFX Houdini. It is expected that the intern will learn some of these technologies while doing his internship.

Skills required:
The ideal student enjoys mathematics, linear algebra, and programming in either C++ or Python. Knowledge in Computer Graphics and Deep Neural Networks (DNN) are assets. Experience with Blender (or other similar software), with 3D APIs such as OpenGL and Direct3D could be useful, but are not necessary. Knowledge of OpenCV and undergraduate course in image processing, computer vision, and DNN are assets.

While I have project descriptions in both English and French, the knowledge of French is not necessary for the internship.

Research area: This research area focuses on the intersection of Quantum Computing, Artificial Intelligence, and Optimization. The project investigates how quantum-inspired and hybrid quantum-classical optimization techniques can support hyperparameter tuning for machine learning models. The broader goal is to explore whether emerging quantum optimization methods can reduce search complexity, improve model performance, and provide efficient alternatives to traditional hyperparameter optimization approaches such as grid search, random search, Bayesian optimization, and evolutionary algorithms.

Student roles:
The student will work as a research intern on a project exploring quantum and hybrid quantum-classical approaches for hyperparameter optimization in machine learning. Their role will include reviewing relevant literature on hyperparameter tuning, classical optimization methods, quantum-inspired optimization, and hybrid quantum-classical algorithms.

The student will help design experimental workflows, prepare benchmark datasets, implement machine learning models, and compare different hyperparameter optimization techniques. They will implement classical baseline methods such as grid search, random search, Bayesian optimization, or evolutionary algorithms, and then explore quantum-inspired or hybrid optimization approaches using available simulation tools or quantum computing frameworks.

The student will run experiments, collect performance results, analyze convergence behaviour, and evaluate trade-offs between predictive accuracy, computational cost, and search efficiency. They will also prepare visualizations, document experimental findings, and contribute to research reports or manuscripts.

The student is expected to participate in regular research meetings, present progress updates, and collaborate with the supervising research team. By the end of the internship, the student will gain practical experience in machine learning, optimization, experimental research design, and emerging applications of quantum computing in artificial intelligence.

Skills required:
Applicants should have a background in Computer Science, Software Engineering, Data Science, Mathematics, Physics, or a related discipline. Strong Python programming skills are required. Familiarity with machine learning concepts, supervised learning models, and hyperparameter tuning is expected. Knowledge of optimization algorithms, linear algebra, probability, or quantum computing fundamentals is an asset but not mandatory. Experience with libraries such as Scikit-learn, NumPy, Pandas, Qiskit, PennyLane, or similar tools would be beneficial.

Research area: My specialized research area lies at the intersection of artificial intelligence, computational modeling, and materials informatics. I focus on developing and applying advanced machine learning algorithms, particularly deep learning and graph neural networks (GNNs), to solve complex problems in materials science and chemistry. My recent work involves the integration of structural, electronic, and graph-based representations of materials to enhance predictive modeling of key properties such as formation energy, band gap, and adsorption behavior. I also prioritize model interpretability and reproducibility, incorporating explainable AI tools to provide scientific insights from black-box models.

Student roles:
The student will play a central role in carrying out this project. They will be responsible for implementing and testing the proposed KP-GNN framework, including the knock-off mechanism and the genetic algorithm for hyperparameter optimization.

Their tasks will include data collection and preprocessing from public materials science datasets, model development using deep learning tools (e.g., PyTorch), and running experiments to evaluate model performance. The student will also analyze results, compare the proposed method with existing models, and help refine the approach based on findings.

In addition, the student will contribute to documenting the work, preparing reports, and potentially assisting with manuscript writing for publication. Regular meetings with the supervisor will support progress, troubleshooting, and skill development.

Through this project, the student will gain hands-on experience in machine learning, graph neural networks, and computational materials science.

Skills required:
The student should have a background in computer science, data science, or a related field, with experience in machine learning and deep learning. Proficiency in Python is required, especially using libraries such as NumPy, pandas, and scikit-learn. Familiarity with frameworks like PyTorch or TensorFlow for building deep learning models is important. Prior exposure to graph neural networks is an asset. Although not mandatory, a basic understanding of materials science concepts such as crystal structures or formation energy would be helpful. The student should also have strong problem-solving skills and an interest in interdisciplinary research involving AI and materials chemistry.

Research area: My research focuses on topics related to software reuse, recommendation systems, big data mining in software repositories, testing for continuous system integration in cloud environments, cloud- based architecture microservices and mobile computing and IoT systems, as well as the application of artificial intelligence techniques to software engineering.

Student roles:
The intern will collaborate with graduate students on:
The individual analysis of each topic covered, the preparation of subsequent activities, and the exploration of the various related concepts. The main topics include:
• Serious games
• The use of machine learning in serious games
• The use of augmented reality in serious games
Following this exploration, one or more proposals will be presented to describe different possible solutions. These proposals will then be discussed to select one solution, which will be developed and evaluated.

Skills required:
Mastery of basic software engineering concepts, mastery of fundamental concepts of service-oriented architectures, proficiency in at least one programming language (Python, Java, C++, C#, etc.), and basic knowledge of machine learning, virtual reality, and augmented reality concepts.

Research area: The Applied Machine Learning Research Lab (DAMLR) envisions a synergistic collaboration with industrial partners and research institutes to advance the frontiers of theoretical machine learning and AI research. Focused on pivotal areas such as data representation learning, AI safety, generative AI, and explainable AI, the lab aims to foster innovation through a multidisciplinary approach. By uniting the practical insights of industry with the pioneering theories of academia, the lab seeks to develop robust frameworks and methodologies that push the boundaries of what AI can achieve.

Student roles:
The summer intern will contribute to the design and prototyping of a long-term memory system for LLM agents, focusing on bridging the gap between existing retrieval/compression approaches and more persistent, structured memory representations. The student will work at the intersection of LLM systems, data engineering, and applied machine learning.

The role involves implementing core components of an external memory pipeline, including data ingestion, schema design, and storage (e.g., vector databases and/or structured memory graphs). The student will experiment with memory encoding strategies (e.g., embeddings, structured triples, or hybrid representations), and develop retrieval and update mechanisms that support incremental memory growth across multi-session interactions. They will also explore lightweight consolidation or pruning strategies to manage memory quality over time.

On the modeling side, the student will integrate the memory system with an LLM-based agent (e.g., via APIs or frameworks such as LangChain/FastAPI), enabling the agent to read from and write to long-term memory during task execution. The student will design and run experiments to evaluate memory effectiveness using existing benchmarks such as LongMemEval, as well as simplified custom tasks that test persistence, recall accuracy, and consistency across sessions.

The student is expected to have strong programming skills in Python, familiarity with LLM APIs, and basic knowledge of machine learning or information retrieval. Experience with databases, backend systems, or knowledge graphs is a plus but not required. More importantly, the student should be comfortable working in a research-oriented environment, able to iterate quickly on prototypes, and communicate findings clearly through documentation and reports.

By the end of the internship, the student will deliver a working prototype, experimental results, and recommendations for scaling the system toward production or further research.

Skills required:
A strong candidate should demonstrate excellent Python programming skills and be proficient in using AI-assisted development tools such as GitHub Copilot and OpenAI Codex to support implementation.

The candidate should have a solid understanding of how Large Language Models (LLMs) work, including their training processes and underlying objectives.

In addition, prior hands-on experience with agentic systems—such as OpenClaw or similar frameworks—is highly desirable.

Research area: My research group focuses on compiler optimization and automatic hardware generation for machine learning and high-performance computing. We develop techniques that automatically transform programs into efficient implementations for CPUs, GPUs, and specialized accelerators such as FPGAs. Current research topics include sparse and compressed tensor computations, equality-saturation-based optimization, domain-specific languages, and hardware accelerator generation. Our goal is to make advanced computing systems easier to program while improving performance and energy efficiency.

Student roles:
The student will participate in the design and implementation of a sparse tensor algebra rewrite library. Their responsibilities will include studying relevant literature, implementing compiler transformations and rewrite rules, developing software prototypes, and evaluating the resulting system on representative sparse tensor workloads.

The student will work closely with the research team, participate in regular meetings, present progress updates, and contribute to the analysis and interpretation of experimental results. Depending on progress and interests, the student may also contribute to the preparation of technical reports, open-source software artifacts, or research publications.

Throughout the project, the student will gain hands-on experience with modern compiler infrastructures, program optimization techniques, and machine learning while contributing to an active research project.

Skills required:
The ideal candidate will have a background in computer science, computer engineering, or software engineering. Applicants should be comfortable programming in multiple languages, have completed a course in algorithms, possess a solid understanding of data structures, and have a good foundation in linear algebra.

Prior exposure to compilers or program analysis would be an asset. Experience with machine learning systems or high-performance computing is also welcome but not required. Experience with Python, C++, Rust, or similar programming languages is desirable. Familiarity with Linux-based development environments and version control systems (e.g., Git) is required.

Research area: Several Canadian provinces and other countries have legislation designed to address discrimination against persons with disabilities in the workplace. The purpose of this project is to develop recommendations on accessible and inclusive virtual and blended service provision models for the Federal Public Service and federally regulated industries in post-COVID-19 Canada. The recommendations will address sensory disabilities that involve changes in sense processing (vision and hearing), inclusive of sight loss, hearing loss, dual sensory loss, brain injury, neurodiversity (autism spectrum), and learning disabilities.

Student roles:
The required role of the student would be tasked with pulling together materials for the literature review on these topics: Accessibility policies and related technical standards.

Skills required:
The students should have a background in Law-Business or Computer Science/Information Technology.

Research area: My main research program is on developing new AI tools to support software developers, mostly on repairing and finding bugs. Large Language Models have been widely adopted in the recent years to autonomously generate code, yet there are very few studies on their impact on code quality, security and developer's productivity. It is important to measure and understand the effect of such AI tools on human developers' behavior before continuing proposing new AI solutions.

Student roles:
The intern would help gather data (mining GitHub repositories), write code, and categorise our extracted data.
Then the intern will help analyse and explain the data. Eventually, the goal is to (1) design better prompts or support documents to help AI coding agent perform better and (2) design a tool to help developer better guide and review coding agents' changes. The student will also be encourage to participate to campus activities, including 3 minute thesis presentations and weekly seminar presentations.

The student will be leading his own project, but will be working with 3-4 other undergrad students (both local and internationals) who would have their own related projects. Depending on progress of each student, the student may join another project in a support role.

Skills required:
Required: Python, git, good programming skill and understanding.
Desired: Linux, AI, bash script.

Research area: The proposed research project applies the Universal Design (UD) framework in a social robot-aided training system, supporting Visually Impaired Individuals (VII) in attaining an inclusive job interview process. The UD framework advocates that any given environment is accessible by all people, including persons with disabilities, to the greatest extent possible, without the need for adaptation or specialized design. A research evidence-based approach will systematically review related literature in empirical research on social robot-aided training with VII and the interview skills training. Also, we will collect feedback from VII with successful and unsuccessful experiences in the job interview process.

Student roles:
The required role of the student would be tasked with pulling together materials for the literature review on this topic and conduct related experiments.

Skills required:
The students should have a background in Business, Social Science or Computer Science/Information Technology.

Research area: My research area is medical image analysis, a discipline at the intersection of computer vision, machine learning and mathematics. In particular I specialize in analyzing brain magnetic resonance images (MRI) of the human brain. I look at both image data and geometric data extracted from images (e.g. shapes). We use modern deep learning methods for medical image segmentation and analysis. My current focus is anatomical shape analysis and population studies.

Student roles:
The students will be responsible of
• doing a literature review on related topics: variational deep shape auto encoders, graph convolutional networks, normative models in medical imaging
• understanding our previous work on the hippocampus model - both theory and code
• apply and modify our method for the new brain surface data
• visualize and interpret results together with our medical collaborators
• summarize the method and results in a publication

Skills required:
The ideal candidate has a background and interest in medical image analysis and deep learning. The student should have taken a machine learning course and have some experience with deep learning models. Good mathematical background is also expected.

Research area: I am working on combining swarm intelligent algorithms (SIAs) with image segmentation approaches to deal with various image datasets. Also, I combined SIAs with machine learning approaches to deal with various datasets. I like to work on early diagnosis or detect breast or skin cancer with computer-aided skills

Student roles:
The student is expected to know some skills of programming like Python, or R, or MatLab and swarm
intelligence algorithms (is able to learn it quickly if s/he does not know). The student will write many
computer programs by using Matlab software or python and he or she will apply swarm intelligence algorithms
such as Genetic algorithm, Particle Swarm Optimization Algorithm, differential evolution algorithm,
spider, bat, and other algorithms. Students will compare these algorithms with other algorithms. Also,
students will help in writing a report and graph as research assistants.

Skills required:
Writing extensive computer programs in Python, or R, or Matlab, Knowing swarm intelligence algorithms and optimization will be assets.
Knowing image segmentation, clustering, and swarm intelligent algorithms

Research area: Astrophysical simulations are essential for understanding phenomena such as star formation, planet formation, accretion discs, and the evolution of gas around stars and black holes. My research focuses on the numerical algorithms used in these simulations, particularly smoothed particle hydrodynamics (SPH), a particle-based method for solving the equations of fluid dynamics. I develop and test algorithms for fluids, gravity, magnetic fields, and related physics. A major focus is understanding the accuracy, stability, and computational efficiency of these methods, so that simulations can be trusted to produce scientifically meaningful results.

Student roles:
You will develop and test a machine-learning model that identifies whether individual SPH particles are located in shocks. The model will decide this using the properties of a particle and its neighbours, similar to the information available inside an SPH simulation code. This makes the project a local, per-particle classification problem rather than a global analysis of an entire simulation.

The first part of the project will involve creating training data. You will generate three-dimensional particle neighbourhoods with different levels of disorder, including relaxed distributions, randomly perturbed distributions, and anisotropic neighbour arrangements. Known velocity, density, and pressure fields will then be assigned to these particles. These fields will include true shocks, but also non-shock cases such as shear flows, vortices, contact discontinuities, smooth compression, and gravitational-collapse-like flows. This is important because a useful shock detector must not confuse particle disorder or smooth compression with a real shock.

You will then develop machine-learning models that operate on the local neighbourhood of each particle. A central part will be implementing a small neural-network architecture for unordered particle sets, where the same network is applied to each neighbour and the resulting information is combined to classify the central particle. Simpler models, such as decision trees or gradient-boosted trees, may be used as baselines, but the main focus will be learning directly from the local neighbour structure.

The trained models will be compared against existing SPH shock indicators and artificial-viscosity switches. The main goal will be to determine whether machine learning can reduce false shock detections while still correctly identifying true shocks. You will analyse the accuracy, robustness, and interpretability of the learned model, and identify which local particle features are most useful. A successful project will produce a tested shock-classification model and recommendations for incorporation into an astrophysical SPH code such as Phantom.

Skills required:
This project is suited for a student interested in combining astrophysics, numerical methods, and machine learning. Students from physics, astronomy, computer science, mathematics, or data science backgrounds are welcome. Prior programming experience is needed, preferably in Python. Some basic knowledge of machine learning, such as training and validating supervised models, is expected. Experience with neural networks in PyTorch, TensorFlow, or JAX would be an asset. Experience with scientific computing, vector calculus, or fluid dynamics would also be beneficial.

Research area: My recent research interests are best described as computational optimization and their applications, modeling and numerical analysis, metaheuristic/ evolutionary computing/artificial intelligence algorithms and their applications in engineering. In particular, I am interested in applying these algorithms to global optimization arising from output feedback controller design (continuous and discrete), mechanical engineering, wireless sensor network, data mining and datasets, and various optimization problems (my recent proposal for discovery NSERC.

Student roles:
The student is expected to know some skills of programming like Python, MatLab or R software and Metaheurtics algorithms (is able to learn it quickly if s/he does not know). The student will write many computer programs by using Matlab or R software or Java and he will apply various Ant Colony Optimization algorithms, Hybrid Genetic Particle Swarm Optimization Algorithm, and Hybrid Simulated Annealing to datasets from UCI. He will compare these algorithms with each other algorithms. Also, students will help in writing a report and graph as research assistants. After I am going to propose a new metaheuristic algorithm and show this proposed algorithm is outperforms other algorithms in the literature. Our target is to get this work published in refereed journals and present it at a conference.

Skills required:
Students know Matlab software and/python/and or R. Knowing metaheuristic algorithms and machine learning will be an asset

Research area: This research project focuses on the design and evaluation of assistive technology for people with cognitive impairments. The project will focus on the use of artificial intelligence models to provide graduated multimodal assistance on mixed-reality headsets, taking into account the user's context, the task in hand and the user's profile. The project will integrate design activities in artificial intelligence, mixed reality and human- computer interaction.

Student roles:
The intern will join the DOMUS Laboratory team at the Université de Sherbrooke. His role will be to learn about the research project and the systems used, by reading reports, scientific articles and using tutorials. You will then have to develop and evaluate sections of code that you have improved or designed, in collaboration with a team of graduate students and research professionals. You will be required to document your work using the lab's existing tools and systems. He/she may also participate in the writing of scientific articles and communications related to the results of the research project.

Skills required:
Good programming knowledge and experience in languages such as Python, C# or C++.

In-depth knowledge of artificial intelligence, particularly in the use of deep learning models, large language models (LLM) and machine learning.

Experience in Unity development and mixed reality/virtual reality development is an asset.

Research area: Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set.

Student roles:
The intern will have to convert the quadratic knapsack model into an equivalent Ising model and set up a QAOA algorithm framework for solving the model.
The intern will then have to conduct some computational experiments in order to explore the quality of the solution given by QAOA as well as its scalabity on quantum computers.

Skills required:
Linear algebra
Computer programming
Quantum computing (a good knowledge is required)

Research area: My research focuses on advancing the development of high-performance, highly scalable, and highly available distributed systems, which serve as the foundation for modern computing infrastructures. In particular, I study how to design efficient algorithms, protocols, and system architectures that power distributed AI systems and database management systems (DBMS). Our work bridges AI, databases, and cloud systems, addressing challenges in scalability, efficiency, and real-world deployment, with the goal of enabling next-generation intelligent data systems.

Student roles:
The student will contribute to the design, implementation, and evaluation of a multi-agent AI system for data engineering. The role involves building and integrating components such as data retrieval pipelines, RAG-based document access, and LLM-powered agents for tasks like query generation, pipeline automation, and data analysis.

The student will be responsible for coding and system development, including constructing the RAG pipeline (indexing, retrieval, and integration with LLMs) and experimenting with fine-tuning or adapting LLMs for data engineering tasks. They will also design simple coordination mechanisms between agents to enable collaborative workflows.

A key part of the role is experimental evaluation, where the student will measure system performance in terms of accuracy, efficiency, and usability across different tasks and datasets.

The student will work closely with graduate students, participating in regular discussions and receiving mentorship. This role emphasizes hands-on system building, experimentation, and gaining practical experience with LLMs, RAG systems, and agent-based architectures.

Skills required:
Students should have a background in computer science or computer engineering. A solid understanding of data structures and algorithms and basic knowledge of machine learning are required. Programming experience (e.g., Python) is expected. Familiarity with databases (e.g., SQL) and data processing is preferred. Prior exposure to LLMs, RAG, or AI systems is a plus but not required. Interest in building systems and working with modern AI tools is highly beneficial.

Research area: This research investigates the integration of artificial intelligence into engineering design education, with a focus on proactive safety thinking. While engineering curricula emphasize functional and economic design criteria, hazard identification and risk mitigation are often underdeveloped skills in student designers. Our lab explores how AI-powered decision-support tools can be embedded into the design process itself, prompting students to systematically consider safety risks before they become real-world consequences.

Student roles:
The intern will investigate and prototype an AI-assisted hazard identification tool for engineering design students. Activities will include: reviewing literature on engineering design education, safety frameworks, and AI decision-support tools; exploring AI and LLM approaches suited to context-aware hazard prompting; designing a system architecture for the tool; developing a proof-of-concept prototype; and conducting informal usability exploration with engineering students if progress allows.
The scope of development will evolve based on research findings and progress; the intern is expected to contribute original thinking to the design problem, not just implement a predetermined solution. The intern will meet weekly with the supervising professor with structured but flexible milestones. This project suits a curious, independent student interested in the intersection of AI, safety, and education.

Skills required:
Applicants should have strong Python skills and experience with LLM APIs or frameworks (e.g., OpenAI, Hugging Face, LangChain, Anthropic Claude API). Familiarity with web or app development is an asset. Background in engineering design, occupational health and safety, or educational technology is a strong plus. Ability to work independently and manage an exploratory research project is essential. The ideal candidate is intellectually curious and motivated by research questions at the intersection of AI, safety, and education.

Research area: My research is in the general area of Human-Computer Interaction. In particular, my research is in the user modelling theme of HCI. My research involves developing applications and games to influence the attitude or behaviour of people for their good and that of society. In addition, my research consists in analysing user-generated data from social media to understand users' behaviour in order to predict future behaviour

Student roles:
The student will contribute to the design, development, and evaluation of the AI Detective mobile game. Responsibilities will include conducting literature reviews on AI literacy, misinformation, and serious games; assisting with game design and implementation; developing educational content and gameplay mechanics; conducting usability testing; and supporting data collection and analysis. The student will also assist in evaluating the game's effectiveness in improving users' ability to critically assess AI-generated information. Through these activities, the student will gain experience in software development, user-centered design, educational technology research, and public-facing AI literacy initiatives.

Skills required:
The ideal student would be an undergraduate Computer Science student with a strong background in programming, mobile application development, and human-computer interaction. Through coursework and project experience, the student has developed skills in software development, user-centered design, and data analysis. The student has a strong interest in artificial intelligence, digital literacy, and the societal impacts of emerging technologies. These skills align well with the proposed project, which combines mobile game development, AI literacy, and public education. Participation in the project will further strengthen the student's technical, research, and communication skills while contributing to an important societal challenge.

Research area: My specialized research area is Responsible AI for agentic and LLM-augmented systems, with a focus on explainability, reliability, safety, and human-centered evaluation. My work studies how AI systems reason, retrieve information, use tools, interact with users, and collaborate as multi-agent workflows. I am particularly interested in understanding and controlling agent behaviour through trace analysis, evidence grounding, failure detection, and transparent evaluation. In healthcare-oriented simulations, this includes studying how clinical agents collect information, communicate across roles, handle uncertainty, and avoid unsafe or unsupported recommendations. The broader goal is to design trustworthy AI systems that are interpretable, accountable, and safe.

Student roles:
The student will contribute to the project through literature review, system design, prototype implementation, scenario creation, experimentation, and analysis. In the first stage, the student will review recent research on medical LLMs, simulated patients, clinical reasoning benchmarks, multi-agent hospital simulations, and responsible AI in healthcare. The student will also examine suitable open-source medical case datasets and help select a small number of cases that can be safely transformed into synthetic patient journeys.

In the second stage, the student will help design the simulated hospital workflow. This will include defining the roles of the patient, triage nurse, physician, lab or imaging assistant, pharmacist, discharge coordinator, and safety reviewer. The student will specify what each agent can know, what questions it can ask, what actions it can take, and when it must escalate uncertainty or risk. The student will also help convert medical cases into interactive patient profiles, where information is revealed gradually through nurse and doctor questioning.

In the third stage, the student will implement the prototype using Python and available LLM or agentic-AI tools. The system will record structured traces of the patient journey, including symptoms collected, questions asked, triage decisions, handoffs, tests requested, differential diagnoses, treatment or prescription recommendations, safety checks, and final outcomes. The student will run controlled experiments across selected medical cases and compare different workflows, such as doctor-only interaction, nurse–doctor collaboration, and nurse–doctor collaboration with a safety-review agent.

In the final stage, the student will evaluate the system using responsible-AI criteria. The analysis will focus on diagnostic reasoning, information completeness, handoff quality, unsafe or unsupported recommendations, missed red flags, escalation behaviour, and explainability of decisions. The student will prepare documentation, case studies, and a final report. Regular supervision will ensure that the work remains feasible, ethical, and clearly positioned as research simulation rather than clinical deployment.

Skills required:
The ideal student should have strong Python programming skills and basic knowledge of artificial intelligence, machine learning, natural language processing, or software engineering. Experience with large language models, APIs, agent frameworks, simulation, or data analysis would be helpful but is not required. The student should be comfortable reading research papers, implementing prototypes, designing controlled experiments, and analyzing results. Interest in responsible AI, healthcare AI, human-computer interaction, or multi-agent systems is highly desirable. The project does not require medical training, but the student must be careful, ethical, and willing to work only with public or synthetic cases.

Research area: This research explores the application of large language models (LLMs) to improve the accessibility and comprehension of workplace safety documentation. A significant challenge in occupational health and safety is that critical procedures are often written at reading levels inaccessible to many workers, particularly those with limited literacy or for whom English is a second language. Our lab investigates AI-driven plain language transformation, automatically simplifying complex safety content while preserving technical accuracy and regulatory compliance.

Student roles:
The intern will investigate and prototype an LLM-based safety procedure simplification tool. Activities will include: reviewing relevant literature on plain language transformation and LLM prompting strategies; exploring and comparing LLM frameworks; designing and developing a proof-of-concept web application that accepts safety procedure text and returns a simplified Grade 4 version; and evaluating output quality using automated readability metrics on a sample set of safety documents.
The depth of implementation and evaluation will be guided by the intern's progress and research findings; meaningful exploration is valued over feature completeness. The intern will meet weekly with the supervising professor and collaborate with graduate students on related NLP projects. This project suits a student who enjoys combining software development with applied research questions.

Skills required:
Applicants should have strong Python programming skills and hands-on experience with at least one LLM framework or API (e.g. OpenAI API, Anthropic Claude API). Basic web development skills (React, Flask, or similar) are required to build the application interface. Familiarity with text processing or NLP concepts is an asset. No prior background in occupational safety is needed; curiosity and willingness to learn the domain is sufficient. The ideal candidate is comfortable working independently on a defined build-and-evaluate project with clear milestones.

Research area: I am interested in human-centric research, especially relating to privacy and security; including user-centric privacy and cybersecurity, human-centric software security, and privacy and security in emerging technologies.

Student roles:
The student will conduct this research, including, literature reviews, implementation (e.g., crafting phishing emails using generative AI), testing with human participants, and analyzing and interpreting data. The supervisor will work with the student to scope the project, and follow-up on the progress.

Skills required:
Students should have programming skills, familiarity with AI, interest in learning new things, and genuinely motivated to work independently on research projects.

Research area: This research examines artificial intelligence as an active participant in organizational decision-making—not only a passive tool. As AI generates recommendations, forecasts, and insights, it shapes how problems are framed, evaluated, and enacted, raising questions about authority, trust, delegation, and accountability. The work studies how humans and AI negotiate decision authority, how trust and reliance form around AI outputs, how responsibilities shift when tasks are automated, and how accountability is attributed across people and systems. It draws on organizational theory and the sociology of technology to treat AI as embedded in social and institutional contexts.

Student roles:
The student will support experimental studies of how individuals respond to AI-generated recommendations under varying transparency and uncertainty, including literature review, materials preparation, and data handling. The student will assist with computational analyses of behavioral or organizational data where applicable, and help organize qualitative materials from interviews or case documents.

Responsibilities include documenting methods, maintaining reproducible workflows, and contributing to drafts of reports and presentations. Regular supervision meetings will guide priorities, ethics, and interpretation. The student will work independently on assigned tasks while collaborating on integrated research outputs.

Skills required:
Students should have training in organizational behavior, management, sociology, psychology, or related fields, with strong interest in technology and organizations. Quantitative skills for experiments and data analysis (e.g., R or Python) and comfort with qualitative and mixed methods are valuable. Familiarity with AI concepts or human–AI interaction is an asset. Strong writing, research ethics awareness, and critical thinking are essential.

Research area: Municipal and Transportation Asset Management deals with the rolling data collection of massive amounts of data combined with environmental data in order to identify deterioration trends on asset conditions, assess risk of collapse and allocate funds by optimizing early enough maintenance and rehabilitation to achieve long term sustainability and deliver cost effective assets that support sustainability of the community.

Student roles:
Code the solution on RStudio (shiny) to undertake the data mining, data integration, and decision making

Skills required:
data mining, statistical analysis, trend analysis, optimization of large scale problems, coding computer software, integrating (consume) online complementary data, specially environmental.

Research area: This project sits at the intersection of data science, applied mathematics, and public health. We study how to extract meaningful insights from complex, real-world data—specifically food safety inspection data collected across multiple cities. The research combines data analysis, visualization, and modern computational tools to better understand patterns of risk and support smarter public health decisions. Students will work with large, real datasets and contribute to developing data-driven approaches that have direct impact on how cities protect public health.

Student roles:
You will play a key role in making real-world data usable, insightful, and impactful.

Your primary focus will be on data cleaning, visualization, and exploratory analysis. You will work with datasets from multiple public health units, each with different formats and conventions. You will help transform these raw datasets into clean, well-structured data that can be used for deeper analysis and modeling. This includes handling missing data, resolving inconsistencies, and organizing information into meaningful formats.
A major part of your role will be to explore and visualize the data. You will create visualizations that reveal patterns over time (e.g., how violations evolve), across locations (e.g., geographic trends), and across different types of establishments. Your work will help answer key questions and guide the direction of the research.

You will be part of an active, collaborative research team. You will work closely with graduate students and postdoctoral researchers, attend group meetings, and present your findings. You will also gain exposure to how data science is applied in real-world public health settings through interactions with partner organizations.

This role is ideal for a student who wants to move beyond textbook datasets and gain hands-on experience with complex, real-world data, while contributing to a project with tangible societal impact. Your work will form a critical foundation for the predictive models and decision-support tools developed in the broader project.

Skills required:
We are looking for students with a background in mathematics, statistics, computer science, or a related field who are excited about working with real-world data. Experience with Python or R is required, particularly for data manipulation and visualization (e.g., pandas, matplotlib, ggplot2). Familiarity with basic statistics and exploratory data analysis is expected. Curiosity, attention to detail, and a willingness to work through messy datasets are essential. Prior experience with data cleaning or visualization is an asset, but not required.

Research area: This project is in precision health and data science, focusing on how diet, the gut microbiome, and cardiovascular health are connected. It combines large-scale biological data (microbiome, genetics, health markers) with computational methods and AI to understand how food affects individuals differently. The research aims to translate complex biological and behavioural data into practical, personalized dietary insights. Students will work at the interface of health, data science, and nutrition, contributing to research that has the potential to improve how people eat and manage cardiovascular risk.

Student roles:
You will play a key role in helping transform complex biological and dietary data into meaningful insights.

Your main responsibilities will focus on data cleaning, integration, visualization, and exploratory analysis. You will work with multi-source datasets that may include dietary records, microbiome features, and health indicators. These datasets are rich but often messy—your work will help make them usable for analysis and modeling.

You will develop workflows to clean and organize data, including handling missing values, aligning variables across datasets, and structuring data for analysis. This is a critical step in enabling the advanced modeling work carried out by the research team.

A major part of your role will be exploring and visualizing the data. You will create visualizations to examine patterns across individuals and groups—for example, variation in dietary habits, relationships between food intake and microbiome features, or trends in health indicators. You will also contribute to summarizing findings in clear and compelling ways that can be shared within the research team and with collaborators.

You will work closely with graduate students, postdoctoral fellows, and the principal investigator, and will be part of a collaborative, international research environment. You will participate in group meetings, present your work, and receive feedback from researchers with expertise in data science, health, and nutrition.

This role is ideal for a student interested in applying data science to real-world health challenges. You will gain hands-on experience working with complex, interdisciplinary datasets, strengthen your programming and analytical skills, and contribute to research that aims to shape the future of personalized nutrition and digital health.

Skills required:
We are looking for students with a background in mathematics, statistics, computer science, data science, or a related field. Experience with Python or R is required, including data manipulation and visualization (e.g., pandas, matplotlib, ggplot2). Basic knowledge of statistics and data analysis is expected. Interest in health, nutrition, or biological data is an asset but not required. Strong curiosity, attention to detail, and willingness to work with complex, real-world datasets are essential.

Research area: My research focuses on artificial intelligence, machine learning, deep learning, quantum computing, and predictive analytics for energy systems and industrial applications. We develop advanced forecasting methodologies that leverage large-scale time-series data to support energy planning, operational decision-making, and resource optimization. Current research investigates ensemble learning, transformer architectures, explainable AI, and quantum-inspired optimization techniques to improve forecasting accuracy, robustness, and computational efficiency in complex energy systems.

Student roles:
The student will contribute to the development of ensemble forecasting models using deep learning and transformer architectures. Responsibilities include data preprocessing, exploratory analysis, feature engineering, implementation of forecasting models, experimentation with quantum-inspired optimization techniques, uncertainty analysis, and performance evaluation. The student will compare classical and hybrid quantum-enhanced approaches for model tuning and ensemble construction, identify key demand drivers, and contribute to technical reports, presentations, and scientific publications.

Skills required:
Students should have a background in computer science, electrical engineering, data science, applied mathematics, statistics, physics, or related disciplines. Experience with Python programming, machine learning, deep learning, or time-series analysis is desirable. Familiarity with neural networks, transformer architectures, optimization methods, or energy systems is beneficial. Exposure to quantum computing concepts or platforms such as Qiskit is an asset but not required. Students interested in AI, forecasting, and emerging quantum technologies are encouraged to apply.

Research area: Software engineering, assistive technology design, extended reality

Student roles:
The student interns will play an integral role in the design, development, and evaluation of an AI-assisted Extended Reality (XR) typing training system for nonspeaking autistic individuals. They will work as part of a multidisciplinary research team to help build an accessible and personalized XR-based learning environment aimed at improving communication autonomy for a historically underserved population.

Key responsibilities include:

Software Development: Assist in implementing core system components using Unity or Unreal Engine, including the virtual letterboard interface, real-time feedback modules, and XR interaction mechanisms.

AI and Behavior Analysis: Collaborate on the integration of AI models that adaptively guide users based on their behavior. This may involve data preprocessing, fine-tuning machine learning models, and evaluating performance metrics in collaboration with research leads.

Hardware Integration: Support system integration with XR devices (e.g., Meta Quest, HoloLens), including setup, calibration, and testing across various interaction contexts.

Usability Testing and Iteration: Participate in co-design sessions and pilot evaluations with therapists, caregivers, and autistic users. Assist in preparing study materials, capturing observations, and iterating on system design based on feedback.

Documentation and Collaboration: Maintain clear documentation of code, design decisions, and study outcomes. Collaborate regularly with research staff, faculty advisors, and community partners through team meetings and progress reviews.

This role provides a unique opportunity for students to apply their technical skills in a socially impactful research environment. Students will gain hands-on experience in XR development, adaptive AI systems, and accessible interface design while contributing to a project that aims to transform communication access for nonspeaking autistic individuals. A strong interest in inclusive technologies, ethical design, and interdisciplinary teamwork is essential for success in this role.

Skills required:
The ideal student will have a background in Software Engineering, Computer Science, or a related field. Strong programming skills in C#, Python, or JavaScript are required. Experience with Unity or Unreal Engine for XR development is preferred. Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) or computer vision tools (e.g., OpenCV) is an asset. Applicants should be comfortable working with XR hardware and have an interest in assistive technologies or inclusive design. Strong communication skills and the ability to work in an interdisciplinary, user-centered environment are essential. Prior experience with accessibility or neurodiversity-related projects is a plus.

Research area: My research area is AI-assisted game economy design, player experience evaluation, and game user research. It combines virtual economy modelling, Machinations-based system representation, machine learning, player archetypes, and design-support tools to evaluate how resource flows, rewards, scarcity, progression, sinks, converters, and monetization-related structures affect player motivation, fairness, engagement, and long-term retention across game genres.

Student roles:
The interns will support the project mainly through data gathering, economy modelling, dataset validation, and validation preparation. Their first responsibility will be to research a diverse selection of commercial games across different genres and document how their internal economies work. This will involve reviewing reliable public sources such as official game documentation, patch notes, game wikis, design breakdowns, gameplay videos, walkthroughs, player guides, academic sources, and direct gameplay observation when available. For each selected game, interns will identify the major resources, currencies, progression systems, taps, sinks, converters, inventories, traders, reward structures, costs, cooldowns, unlocks, scarcity mechanisms, and feedback loops.
Interns will then translate these findings into structured economy documentation and, where appropriate, Machinations-style economy models or model-ready templates. They will help ensure that each model is complete, understandable, consistently labelled, and suitable for use as training, validation, or test data. They will also annotate potential player-experience implications, such as whether the economy may create excessive grind, weak motivation, unfair progression, resource hoarding, inflation, bottlenecks, unclear goals, or overpowered strategies.
During the validation phase, interns will assist with checking dataset quality, reviewing whether economic entities and resource flows have been represented correctly, and comparing model outputs against expected design diagnoses. They may help create controlled test scenarios by modifying economy parameters, such as increasing tap output, weakening sinks, raising upgrade costs, reducing reward frequency, limiting inventory capacity, or changing conversion rates. They will also help apply evaluation rubrics, summarize error patterns, prepare tables and reports, organize validation materials for expert review, and document whether the framework’s recommendations are relevant, feasible, clear, and aligned with player-experience goals.

Skills required:
Interns should have a background in computer science, software engineering, game development, HCI, data science, or a related field. Useful skills include analytical reading, game systems thinking, data organization, basic Python or spreadsheet work, familiarity with games across genres, and interest in player experience, economy design, or AI-supported design tools.

Research area: My specialized research area is applied artificial intelligence for practical decision-support systems. My work focuses on developing lightweight and interpretable AI methods, including machine learning, natural language processing, computer vision, and optimization, for real-world domains such as construction, health communication, and digital workflow automation. I am especially interested in AI systems that can operate under practical constraints, support human decision-making, and translate research methods into usable tools for industry, community, and public-sector applications.

Student roles:
The student will contribute to the design, implementation, and evaluation of an AI-assisted panelization framework for architectural designs. Their role will begin with a focused review of the PAAD framework and related research in evolutionary optimization, constraint satisfaction, and design automation. They will then help set up the software environment, review any existing code or data structures, and define the benchmark cases and evaluation metrics for the project.

The student will implement or extend a baseline PAAD-style optimization pipeline and use synthetic architectural layouts to test panelization under varied geometry and manufacturing constraints. A central part of the role will be to design and evaluate an AI-assisted extension, such as adaptive heuristics, complexity-aware repair strategies, or guided search methods that improve solution quality or runtime efficiency.

The student will conduct experiments, analyze trade-offs among material usage, manufacturability, correction requirements, and runtime, and document the results in a clear technical report. They will also participate in regular supervision meetings, provide progress updates, maintain organized code and experimental records, and prepare final presentation materials. The role is intended to give the student hands-on experience in applied AI research, optimization, prototype development, and experimental evaluation in a practical construction-related setting.

Skills required:
The student should have a background in computer science, engineering, mathematics, or a related field, with strong programming skills, preferably in Python. Prior coursework or experience in artificial intelligence, machine learning, optimization, algorithms, or data structures would be valuable. Familiarity with evolutionary algorithms, constraint satisfaction, synthetic data generation, or design/CAD-related workflows would be helpful but is not required. The student should be comfortable with independent research, experimental evaluation, and developing prototype code, and should have an interest in applied AI for practical engineering or construction-related problems.

Research area: My research focuses on artificial intelligence, computer graphics, computer vision, and geometric modeling. We develop machine learning methods that enable computers to understand, generate, and manipulate 2D and 3D visual content. Current research topics include generative AI for 3D content creation, geometric deep learning, 3D reconstruction, digital fabrication, and human-AI collaboration for design. Our goal is to make advanced design and modeling technologies more accessible to both experts and non-experts.

Student roles:
The student will participate in all phases of the research project, including literature review, dataset preparation, software development, experimentation, and evaluation. Responsibilities include developing algorithms for 3D geometry analysis and parametric model generation, implementing and testing machine learning models, conducting experimental studies, analyzing results, and documenting findings. The student will work closely with the research team, participate in regular meetings, and contribute to technical reports, software prototypes, and potential research publications. The student will also present project progress and final outcomes to the research group.

Skills required:
Applicants should have a strong background in computer science, computer engineering, mathematics, or a related discipline. Experience with Python programming is required. Familiarity with machine learning, computer graphics, computer vision, CAD systems, or 3D geometry processing is desirable but not mandatory. Experience with deep learning frameworks such as PyTorch, as well as basic knowledge of linear algebra and geometry, will be considered an asset. Previous research experience in parametric-based 3D modeling, CAD model generation, procedural modeling, or geometric modeling will be considered a valuable asset. Strong problem-solving, communication, and teamwork skills are expected.

Research area: This project focuses on developing techniques that combine generative AI capabilities with formal reasoning, programming languages, and program analysis techniques, to improve the quality of AI-assisted software engineering agents.

Student roles:
The tasks will include developing software and experimental infrastructure, running experiments, analyzing and reporting results, and reading and discussing research manuscripts. The student will also analyze existing academic and industrial work, including the ability of LLM to assist with software engineering tasks.

At the end of the project, the student will be familiar with the AI-for-code domain and will be able to analyze and discuss major ML reliability considerations. The student will also be familiar with LLMs and will learn to build high-quality software systems. The student will join weekly project meetings with the group and the PI, will be exposed to ways research groups operate, and will have the chance to collaborate internally and with external partners.

Skills required:
The student is expected to:
- have solid programming experience and good command of Java, C++, C, or a similar language;
- be able to learn new programming languages and frameworks;
- have deep understanding of a variety of machine learning techniques;
- have deep understanding of algorithms and data structures;
- be detail-oriented and thorough.

Additional desirable skills (optional):
- experience with program analysis and mobile analysis frameworks (such as soot or asm).

Research area: My research focuses on software engineering and security for embedded and cyber-physical systems, with growing interest in AI-assisted methods that support developers in writing, testing, and securing safety-critical software. I have published on functional reuse in embedded software, security analysis of field-programmable gate-array designs, and agile development pipelines for embedded systems. I am increasingly interested in AI-assisted vulnerability analysis for CPS, where hardware constraints fundamentally change whether a software defect is actually exploitable in practice. This direction addresses a known limitation of current vulnerability scoring systems, which assume a generic computing environment and ignore the resource-constrained reality of cyber-physical platforms.

Student roles:
The intern will own the design and implementation of the AI-assisted vulnerability analysis framework from end to end, including the choice of static analyzers, the design of the hardware profile representation, the design of the language model reasoning pipeline, and the selection of evaluation benchmarks. The supervisor will provide research direction, weekly mentorship, and writing guidance throughout the project.

Weeks 1 and 2: Onboarding, literature review on vulnerability scoring, hardware-aware security analysis, large language model applications in software security, and continuous integration tooling. The intern will produce a focused literature synthesis and a refined system architecture.

Weeks 3 and 4: Construct a benchmark corpus of representative open-source cyber-physical firmwares drawn from ecosystems such as Zephyr, FreeRTOS, and Arduino libraries. Define structured hardware profiles for at least five representative platforms, capturing memory size, interrupt budget, scheduler behaviour, and memory protection.

Weeks 5 and 6: Integrate static analysis tools into a reproducible analysis pipeline. Build the retrieval-augmented generation component grounded in chip datasheets and platform reference manuals.

Weeks 7 and 8: Implement the language model reasoning layer. For each combination of vulnerability finding, firmware, and platform profile, the system produces a context-grounded re-ranked severity along with a textual justification.

Weeks 9 and 10: Run the full evaluation across the benchmark corpus and platform profiles. Measure ranking quality against the standard severity-based baseline and against a small set of expert ratings provided by the supervisor and an external collaborator.

Weeks 11 and 12: Conduct statistical analysis. Draft a short paper. Prepare the open-source release including the framework, the benchmark, hardware profiles, prompts, and reproducibility documentation.

Skills required:
Strong Python programming and comfort with reading C or C++ code. Familiarity with at least one large language model API such as Anthropic, OpenAI, or open-source models served through Ollama. Familiarity with retrieval-augmented generation pipelines is important. Coursework or projects in software security, static analysis, or software engineering is strongly preferred. Experience with at least one static analysis tool such as CodeQL, Semgrep, or clang-tidy is an asset but can be learned. Comfort with Git, Linux, and continuous integration tools such as GitHub Actions is required. Interest in empirical software engineering research is strongly valued.

Research area: My research focuses on intelligent wireless communication networks, IoT systems, Age of Information, network science, and machine-learning-assisted resource allocation. I study how connected devices, sensors, vehicles, and edge systems can exchange timely and reliable information under limited bandwidth, energy, and computing resources. My work combines simulation, graph-based modeling, data-driven methods, and optimization-inspired algorithms for applications in IoT, vehicular networks, cyber-physical systems, and next-generation wireless communication systems.

Student roles:
The student will be responsible for implementing and evaluating a Python-based wireless network simulation framework for IoT and V2X link selection. The student will begin by reviewing basic concepts related to wireless links, signal quality, IoT/V2X networks, and simple machine-learning classification or prediction methods.

The student will then build a simplified network model with mobile or static nodes, define basic wireless link-quality measures, and implement several baseline link-selection strategies. These may include random selection, nearest-neighbour selection, strongest-signal selection, and simple AI-assisted selection using machine-learning models such as decision trees, random forests, logistic regression, or other lightweight methods.

The student will run simulation experiments under different conditions, such as node density, mobility level, noise level, and link-failure probability. They will compare the methods using performance metrics such as packet success rate, delay, reliability, failed transmissions, and information freshness where applicable.

The student will also create clear plots, document the code, participate in regular research meetings, and prepare a final report and presentation summarizing the methodology, results, and main observations. The role is implementation-focused; the student is not expected to derive new mathematical models or develop advanced wireless communication theory.

Skills required:
The student should have good programming skills, preferably in Python. Familiarity with NumPy, Pandas, Matplotlib, NetworkX, or scikit-learn would be helpful. Basic knowledge of computer networks, wireless communication, probability, data analysis, or machine learning is an asset. The student is not expected to know advanced wireless-channel modeling, optimization theory, or communication theory. A motivated student with experience in coding, simulation, and visualizing experimental results should be able to complete the project successfully.

Research area: My broad area of interest is Software Engineering, with a growing focus on Artificial Intelligence (AI) and its applications. In particular, I am interested in AI for Software Engineering, the use of AI in Electric Vehicles (EVs), and intelligent systems for Autonomous Vehicle Scheduling. My research interests also include Model-Driven Software Engineering, Software Architecture, and Mining Software Repositories. I am particularly interested in leveraging AI techniques to address complex software engineering challenges and to develop innovative solutions in emerging domains such as autonomous and electric mobility.

Student roles:
The successful candidate will be involved in software development and programming, designing and conducting experiments, analyzing results, and contributing to the preparation of scientific reports, technical documents, and research publications.

Skills required:
Required qualifications include programming skills in at least one programming language, preferably Python. Applicants should have some knowledge of and background in advanced calculus. Prior coursework, experience, or a background in Artificial Intelligence (AI) is preferred.

Research area: AI, ML, Systems, Programming Languages

Student roles:
Design, development, presentation and reporting

Skills required:
Programming knowledge, AI/ML Knowledge

Research area: My research area is in Human-Computer Interaction (HCI), with a focus on developing and studying novel interactions with mobile and wearable devices. More specifically, I am interested in exploring users’ needs and limitations while they are interacting with emerging technologies (e.g., smartphones and smart glasses). I tackle these limitations by designing new interfaces and interaction techniques. My methodology is rooted in HCI techniques, where I adopt quantitative and qualitative evaluations to validate the interaction techniques.

Student roles:
The student will be mainly involved in the design and development cycles. S/he will be a part of running user studies to evaluate the efficiency of the developed solution. Along with the help of undergraduate, and graduate students, and other collaborators, the student will analyze data and write reports to publish the results in prestigious conferences/journals. These applied experiences in design and analysis methods will be extremely beneficial for the student as these will help them in preparing for their future careers as researchers, designers, or software developers for many top Canadian and worldwide academies and industries.

In addition to concrete research experiences, students are expected to attend on-campus seminars and workshops to gain knowledge on contemporary subject matters. The student will be expected to participate in regular lab meetings, where they will have the opportunity to present their ideas, discuss research progress, and receive constructive feedback. Additionally, the intern will be encouraged to build industry and academic connections in Canada. Such a collaborative and supportive environment would help students become successful in their future careers.

Skills required:
Undergraduate students with computer science or engineering backgrounds are welcome to apply. The proposed project requires the student to develop an intelligent interface capable of understanding and responding to user behaviors, preferences, and contextual cues in real time. Thus, prior experience with AI/Deep Learning/Machine learning application development is preferred. The intern should have the skills to solve complex problems and troubleshoot issues for potential solutions. While the intern will be expected to work in a team environment, s/he needs to have strong communication skills, both when speaking to people and in writing.

Research area: My specialized research area lies at the intersection of human-relevant neural models, medical imaging, and artificial intelligence. I focus on developing quantitative imaging and computational approaches to study dynamic biological systems, with particular interest in functional neural activity, network organization, and disease-relevant circuit remodeling. My current work uses human iPSC-derived cerebral organoids as an in vitro model to investigate activity-dependent network plasticity. This multidisciplinary approach builds on my broader expertise in AI-driven molecular imaging, MRI, and advanced biomedical data processing. With expertise in applied physics, medical imaging, and machine learning, I aim to transform diagnostic and therapeutic strategies.

Student roles:
The student will contribute to the development of a fully computational, in silico framework for analyzing the relationship between optochemical stimulation patterns and neural network plasticity in cerebral organoids. Their primary role will be to work with pre-existing datasets to build a reproducible analysis pipeline for data preprocessing, signal extraction, feature engineering, network modeling, and predictive analysis. They will then implement or adapt computational tools for ΔF/F₀ extraction, event detection, burst analysis, synchrony quantification, and graph-based functional connectivity analysis. Their role will include model training, validation, visualization of results, documentation of code, and preparation of figures or summaries for presentations and future publications. The outcome of the student’s work will be a computational pipeline and set of testable predictions that can later guide in vitro experiments using human iPSC-derived cerebral organoids. Through this project, the student will gain hands-on experience in AI-driven biomedical data analysis, neural activity modeling, calcium imaging data processing, and graph-based analysis of functional connectivity in organoid-like neural systems. The core project will remain fully computational, but if the student is interested, I can provide optional training in relevant wet-lab techniques and include them in in vitro testing in an organoid model.

Skills required:
The student should have a background in biomedical engineering, physics, computer science, or related fields. Programming experience in Python is essential, particularly with libraries such as NumPy, SciPy, PyTorch, or TensorFlow. Familiarity with neural time-series analysis, calcium imaging data, graph/network analysis, or computational modeling would be a strong asset. The student should be comfortable working with pre-existing experimental or publicly available datasets and developing reproducible computational pipelines for data preprocessing, feature extraction, visualization, and model evaluation. No wet-lab experience is required for the core internship. However, training will be provided if requested and will align with students' goals.

Research area: The Electron-Ion Collider is a US$3B new particle collider facility to be built in the current decade at Brookhaven National Lab, Long Island, New York, by the US Department of Energy. At the EIC, for the first time, polarized electrons will collide with polarized protons and light ions, and unpolarized heavy nuclei at intensities a thousand times higher than at previous (unpolarized) colliders. The EIC is at the center of a global 1400+ scientist effort to answer questions about the origin of mass and spin of the proton, and the dynamics of dense gluon systems.

Student roles:
The student will work in a highly dynamic subatomic physics collaboration, with global participants in all major world regions. The student will develop, along with the rest of the reconstruction development team, initial prototypes which will be evaluated. The prototypes will result in implementation of the machine learning algorithms in the continuous integration and production pipelines of the ePIC collaboration, where they are run reproducibly.

Skills required:
- strong understanding of subatomic physics concepts,
- strong collaborative work ethic, supported by portfolio of git activity,
- C++ and/or Python experience,
- PyTorch experience,
- familiarity with ONNX.

Research area: Dr. Faria Khandaker’s research focuses on Next Generation Networks, Resource Optimization in 6G Networks, Semantic Communication, Internet of Things, Edge Computing and Caching, Edge Intelligence, Value-based Caching, and Economic Caching and Pricing Models. One of Dr. Khandaker’s current research projects focuses on the economic analysis of caching in the Internet of Things (IoT). Another research by Dr. Khandaker focuses on the Quality of Service (QoS)-aware Edge-Cloud Management. Dr. Khandaker is involved with other research projects such as Resource Optimization in Semantic Communication in 6G Networks, UAV-Based Emergency Medical Services with AI-Driven Prioritization, and Resource Allocation in RIS-Assisted UAV-MEC Networks.

Student roles:
The required roles of the student are as follows:
Literature Review: The student must conduct a comprehensive review of existing research on Multi-access Edge Computing (MEC), computation offloading, service/content caching, and AI-driven resource optimization techniques in IoT networks.
System Model Design: The student must design the MEC-enabled IoT system model, including IoT devices, edge servers, service/content caching architecture, and computation offloading framework.
Problem Formulation: The student must formulate the joint optimization problem of computation offloading and service/content caching with objectives such as minimizing latency and energy consumption while improving resource utilization and Quality of Service (QoS).
AI-Based Algorithm Development: The student must develop an AI-driven optimization framework using Deep Reinforcement Learning (DRL) to make intelligent and adaptive offloading and caching decisions under dynamic network conditions.
Simulation and Performance Evaluation: The student must build a simulation environment to evaluate the proposed AI-based framework and compare its performance with existing benchmark schemes using metrics such as task execution delay, energy consumption, cache hit ratio, and bandwidth utilization.
Documentation and Reporting: The student must document the literature review, system model, problem formulation, algorithm design, simulation setup, and performance evaluation results, and the regularly report progress to the supervisor. Based on the novelty and research contributions, the outcomes may be published in a peer-reviewed conference or journal.
Through these activities, the student will gain practical research experience in MEC systems, AI-driven optimization, IoT networking, and intelligent resource management while contributing to the development of efficient next-generation edge computing solutions.

Skills required:
The key qualifications a candidate should possess to successfully complete the project include:
1. Strong programming skills, with a focus on Java, Python, and relevant Artificial Intelligence (AI)/Machine Learning (ML) frameworks.
2. Experience working with large datasets and conducting data-driven research.
3. Excellent analytical and problem-solving abilities, with experience in big data technologies and AI-related research.
4. The candidate should be capable of working both independently and collaboratively, demonstrating initiative, creativity, and a proactive approach to research challenges.
Note: Publications in areas relevant to the research project will be considered a strong asset.

Research area: My research focuses on robotics, artificial intelligence, and autonomous systems, with expertise in embodied AI, human–robot interaction, and intelligent robotic systems. I develop autonomous robots using machine learning, deep learning, computer vision, and control systems for perception, decision-making, and navigation in complex environments. My research includes Large Language Models (LLMs), Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, SLAM, and reinforcement learning for mobile robots, drones, and humanoids. Applications include precision agriculture, industrial automation, assistive robotics, autonomous vehicles, and smart manufacturing, with the goal of creating adaptive and intelligent systems that improve productivity and quality of life.

Student roles:
The student will develop and test AI-based navigation algorithms, integrate LLM/VLA models with ROS/ROS2, process camera and LiDAR data, conduct robot experiments, and analyze system performance.

Skills required:
The ideal student should have a background in robotics, mechatronics, computer science, electrical engineering, or artificial intelligence. The student should possess programming skills in Python or C++ and have familiarity with ROS/ROS2, Linux, and Git. Knowledge of machine learning, deep learning, computer vision, and autonomous navigation is desirable. Experience with Large Language Models (LLMs), Vision-Language Models (VLMs), or Vision-Language-Action (VLA) models is an asset. Familiarity with mobile robots, LiDAR, cameras, SLAM, and sensor fusion techniques is beneficial. The student should demonstrate strong analytical, problem-solving, communication, and teamwork skills, along with an interest in embodied AI and intelligent robotic systems.

Research area: My specialized research area focuses on the intersection of reliability and maintenance, human activity recognition, planning and scheduling, operations research (both deterministic and stochastic) and machine learning and artificial intelligence. I explore the application of advanced mathematical programming, machine learning techniques and AI algorithms to enhance the reliability and maintenance processes in various industries. This involves developing models and algorithms for human activity recognition to understand patterns and behaviors that impact system reliability. Additionally, I investigate optimization techniques based on deterministic and stochastic operations research to improve planning and scheduling of maintenance activities, maximizing resource utilization, and minimizing downtime.

Student roles:
The student's role in the research project would involve conducting in-depth literature reviews to
gain a comprehensive understanding of the current state-of-the-art in machine learning, artificial
intelligence, and reliability and maintenance research. They would be responsible for collecting and
preprocessing relevant data, applying statistical, machine learning or AI techniques to develop
predictive models for equipment failure and remaining useful life estimation. They would need to
implement and fine-tune algorithms, perform experiments, and analyze the results to evaluate the
effectiveness and efficiency of the proposed approaches. Additionally, the student would contribute
to the dissemination of research findings through writing research papers and presenting at
conferences.

Skills required:
The student should have a strong background in data analysis, programming, and stochastic
process.. Proficiency in programming (especially Python) and experience with machine learning
frameworks are important. They should understand statistical analysis and predictive modeling, as
well as operations research for optimizing maintenance planning. Knowledge of reliability
engineering and good communication skills are also valuable.

Research area: Dr. Reda Snaiki is an Assistant Professor in the Department of Construction Engineering at ÉTS Montréal, Université du Québec. His research interests span wind engineering, structural engineering, coastal engineering, climate change, and artificial intelligence. He aims to develop advanced tools for assessing risks associated with extreme winds, in order to design more resilient infrastructure for future climate events.

Student roles:
The student will contribute to the development of an AI-based framework for rapid urban wind-flow simulation. Their tasks will include reviewing the literature on urban wind modeling, surrogate modeling, and graph neural networks; helping prepare and process datasets from simulations, experiments, or urban morphology sources; assisting in the construction of graph-based representations of urban environments; implementing and testing machine learning models for wind-flow prediction; and evaluating model performance against reference data. The student will also help analyze the influence of urban form on prediction accuracy, prepare visualizations of the results, and contribute to the documentation and dissemination of the research through technical reports and, where appropriate, conference or journal publications.

Skills required:
The ideal student should have a background in one or more of the following areas: civil engineering, mechanical engineering, environmental engineering, computer science, data science, applied mathematics, or a related discipline. The student should have programming experience, preferably in Python, and some familiarity with machine learning. Background knowledge in fluid mechanics, wind engineering, computational modeling, or urban climate would be beneficial. Familiarity with deep learning frameworks such as PyTorch or TensorFlow, and exposure to graph neural networks or geometric deep learning, would be an asset, but not mandatory.

Research area: I have published over 110 research articles since 2017 and hold 14 US patents in AI, Assistive Technologies, Medical Imaging, Computer Vision, IoT, Robotics, and Deep Learning. I supervised over 30 undergraduate projects, which resulted in 30+ co-authored publications with those students. I received multiple international awards, including a Gold Medal at the 49th Innovation Exhibition in Geneva and first place at the NEOM Oxagon Hackathon in KSA for assistive tech related to the proposed project. I was named among the Top 2% Scientists by Stanford University in 2024, 2025 and also became an IEEE Senior Member in 2025.

Student roles:
Under my supervision, the student will play an active role in the research, design, development, and evaluation of an AI-driven wildfire risk assessment, air quality monitoring, and community preparedness platform for British Columbia, with a particular focus on the Thompson-Okanagan region. The student will assist in collecting, integrating, cleaning, and analyzing publicly available datasets related to wildfire activity, weather conditions, climate trends, air quality measurements, and geographic information systems (GIS).
The student will contribute to the development and evaluation of machine learning and data analytics models that predict wildfire risk levels and forecast air quality and smoke conditions. This includes data preprocessing, feature engineering, model training, validation, and performance assessment. In addition, the student will support the creation of interactive web-based dashboards, mapping tools, and visualization components that communicate complex environmental information in a clear and accessible manner for community members, emergency planners, and decision-makers.
The student will also participate in geospatial analysis, trend analysis of historical wildfire and climate data, and the development of personalized preparedness resources that help residents better understand and respond to wildfire-related risks. Responsibilities may include integrating multiple data sources through APIs, testing platform functionality, validating system outputs, and documenting research findings and technical developments.
This position provides valuable hands-on experience in artificial intelligence, environmental data science, geospatial analytics, web application development, and climate resilience research. Through collaboration and innovation, the student will gain practical research and software development skills while contributing to a project that addresses a significant environmental and public safety challenge facing communities across British Columbia.

Skills required:
I am seeking a motivated intern student with a background in Computer Science, AI, LLMs, Software Engineering, Data Science, Environmental Science, GIS, or related fields. Ideal candidates should have experience in artificial intelligence, machine learning, data analytics, and web development. Proficiency in Python and familiarity with frameworks such as TensorFlow, PyTorch, or Scikit-learn are desirable. Experience with environmental, weather, air quality, geospatial, or remote sensing data is an asset. Strong analytical, problem-solving, communication, and teamwork skills are essential. An interest in climate resilience, sustainability, and disaster management is highly encouraged for this community-focused research project.

Research area: My work focuses on infrastructure–social–organizational integration to support resilient and equitable infrastructure planning, construction, and recovery. She leverages advanced analytical approaches (artificial intelligence, data analytics, and simulation) to enhance construction productivity, improve infrastructure system performance, and inform climate-resilient mitigation and adaptation strategies for infrastructure investment and policy decisions.

Student roles:
The research assistant will support various aspects of the project, including data collection and processing, literature review, analysis of newcomer settlement patterns and infrastructure systems, and visualization of research findings. The candidate will work closely with the research team to assist in developing AI and spatiotemporal models, contribute to the design of the project platform, and help communicate results through reports, presentations, and visual outputs. The ideal candidate is detail-oriented, self-motivated, and has a general interest in urban planning, data science, or community resilience research.

Skills required:
Skills
-Spatiotemporal data analysis and GIS mapping
-Machine learning and AI modeling
-Data science and statistical analysis
-Urban planning or infrastructure knowledge

Software & Tools
-Python or R for data analysis and modeling
-GIS software (ArcGIS, QGIS)
-Experience building visualization dashboards

Research area: My research focuses on AI-enabled laser diagnostics for energy and propulsion systems. In particular, I develop data-driven methods to enhance Raman and laser-induced fluorescence (LIF) measurements in high-temperature reacting flows. By integrating machine learning with optical diagnostics, this work aims to improve signal quality, noise suppression, and quantitative accuracy in harsh combustion environments. Applications include gas turbine combustion, low-carbon fuels (H₂, NH₃, CH₄), and propulsion systems. The goal is to enable more reliable measurements of temperature, species concentrations, and pollutants, supporting the development of efficient, low-emission energy and propulsion technologies.

Student roles:
The student will contribute to the development of artificial intelligence (AI) methods to enhance laser-based diagnostics for combustion and propulsion systems. The primary role will be to support data-driven analysis of Raman and Rayleigh spectroscopy measurements, with a focus on improving signal quality and extracting accurate thermochemical information.

The student will work with existing experimental datasets, including spectral signals affected by noise, background luminosity, and measurement uncertainty. Responsibilities will include preprocessing raw data, implementing signal conditioning techniques, and developing machine learning models (e.g., regression models or neural networks) to denoise spectra and reconstruct weak Raman signals. The student will also evaluate model performance by comparing AI-enhanced results with conventional analysis approaches.

In addition, the student will assist in visualizing and interpreting results, including generating temperature and species profiles and identifying trends across different operating conditions. Depending on the student’s background, the role may also include generating synthetic datasets or using simplified simulations to support model training and validation.

The student will work closely with graduate student mentors and the principal investigator, receiving guidance on both technical tasks and research methodology. They will participate in weekly meetings, present progress updates, and contribute to discussions on data interpretation and model development.

By the end of the project, the student is expected to produce a well-documented analysis workflow, including code, results, and a final presentation summarizing their contributions. This role provides hands-on experience in applying AI techniques to real-world engineering problems, while contributing to the advancement of next-generation diagnostic tools for clean energy and propulsion applications.

Skills required:
We seek a motivated undergraduate student in engineering, physics, or a related field with an interest in data science, combustion, or optical diagnostics. The candidate should have a solid foundation in mathematics and basic programming skills. Experience with Python is highly desirable, particularly for data analysis and machine learning (e.g., NumPy, SciPy, PyTorch, or TensorFlow). Familiarity with signal processing or spectroscopy is an asset but not required. Strong analytical thinking and problem-solving skills are essential. The student should be able to work independently and collaboratively, and have an interest in applying AI methods to real-world engineering problems in energy and

Research area: Our research group at York works at the intersection of machine learning empowered resource allocation and mobility management solutions for the next generation multi-band wireless communications and sensing networks for applications like environment and climate monitoring, smart agriculture, etc. https://sites.google.com/view/ngwn-research-lab/home

Student roles:
The student will participate in a research project focused on AI-enabled multimodal localization using signals from GNSS, WiFi, and 5G wireless systems. Under the supervision of the research team, the student will contribute to the development, evaluation, and validation of data-driven localization approaches.
Key responsibilities include collecting, organizing, and preprocessing multimodal localization data, such as wireless signal measurements and location labels, and assisting with exploratory data analysis. The student will support the implementation of baseline localization techniques, including signal-strength–based methods (e.g., fingerprinting) and time-based positioning approaches, to understand the strengths and limitations of individual sensing modalities.
The student will assist in designing and training machine learning models for localization and data fusion, using Python-based tools and libraries. This includes experimenting with feature representations, evaluating model performance, and comparing AI-based methods with traditional localization techniques. The student will also help integrate multiple data sources to improve localization accuracy and robustness, particularly in challenging indoor and urban environments
Additional responsibilities include maintaining well-documented and reproducible code, participating in regular research meetings, and summarizing findings through short technical reports or presentations. The student may also contribute to preparing figures, results, or demonstrations for research dissemination.
Throughout the project, the student will develop hands-on skills in wireless systems, localization techniques, and applied machine learning, while gaining experience in research problem-solving, teamwork, and technical communication. The project emphasizes learning and mentorship, and responsibilities will be scaled to the student’s background and experience.

Skills required:
Desired Technical Skills: Basic Python programming and interest in machine learning / data analysis
Curiosity about wireless signals, localization, or multimodal sensing, with willingness to learn new tools

Desired Course(s):
Applicants should be enrolled in an undergraduate degree program in Electrical Engineering, Computer Engineering, Computer Science, Software Engineering, Data • • • Science, or a closely related discipline.
• Relevant coursework includes (or will include)
• Machine Learning or Artificial Intelligence
• Introduction to Signal Processing
• Wireless Communications or Digital Communications
• Probability and Statistics
• Linear Algebra
• Data Structures and Algorithms
• Python Programming or Scientific Computing

Research area: My research area is in Human-Computer Interaction (HCI), with a focus on developing and studying novel interactions with mobile and wearable devices. More specifically, I am interested in exploring users’ needs and limitations while they are interacting with emerging technologies (e.g., smartphones and smart glasses). I tackle these limitations by designing new interfaces and interaction techniques. My methodology is rooted in HCI techniques, where I adopt quantitative and qualitative evaluations to validate the interaction techniques.

Student roles:
The student will be mainly involved in the design and development cycles. S/he will be a part of running user studies to evaluate the efficiency of the developed solution. Along with the help of undergraduate, graduate students, and other collaborators, the student will analyze data and write reports to publish the results in prestigious conferences/journals. These applied experiences in design and analysis methods will be extremely beneficial for the student as these will help them in preparing for their future careers as researchers, designers, or software developers for many top Canadian and worldwide academy and industry.

In addition to concrete research experiences, the student is expected to attend on-campus seminars and workshops to gain knowledge on contemporary subject matters. The student will be expected to participate in regular lab meetings where the student will be given a chance to present his/her ideas, discuss research progress, and receive constructive feedback. Additionally, the intern will be encouraged to build industry and academy connections in Canada. Such a collaborative and supportive environment would help the student to become successful in their future career.

Skills required:
Undergraduate students with backgrounds in computer science, engineering, or related fields are encouraged to apply. The project involves developing an AI-powered AR solution; therefore, experience with AI and AR application development, Unity, or interactive systems is preferred. The ideal candidate should have strong problem-solving and troubleshooting skills, be able to work effectively in a team environment, and communicate clearly both verbally and in writing.

Research area: My research interests are in the disciplines of software engineering and applied artificial intelligence, with applications in cybersecurity. I investigate AI-driven approaches to detect software vulnerabilities, secure the growing class of systems built on large language models, including code-generating assistants and autonomous AI agents, and identify AI-generated and human-driven social engineering attacks. My work addresses the security of AI systems themselves, including vulnerabilities introduced by LLM code generation and the emerging risks associated with agentic AI, alongside the use of AI to detect and counter malicious content and manipulation online.

Student roles:
1. Develop a taxonomy of linguistic trust signals used in phishing and vishing content.
2. Build a multilingual corpus of AI-generated phishing content embedding these trust signals.
3. Annotate the corpus by trust-signal type and language.
4. Train and evaluate phishing detection classifiers across languages.
5. Identify and document cases where detection performance degrades due to linguistic variation.

Skills required:
Programming Competence – Python and familiarity with foundational NLP tooling
Linguistic Background – Formal training in linguistics, computational linguistics, or socio-linguistics; bilingual or multilingual competence strongly preferred
Analytical Skills – Capacity to integrate qualitative discourse analysis with quantitative model evaluation
Other Relevant Skills – Strong academic writing ability in English; familiarity with corpus construction and annotation methodology

Research area: My specialized research area lies at the intersection of human-relevant neural models, medical imaging, and artificial intelligence. I focus on developing quantitative imaging and computational approaches to study dynamic biological systems, with particular interest in functional neural activity, network organization, and disease-relevant circuit remodeling. My current work uses human iPSC-derived cerebral organoids as an in vitro model to investigate activity-dependent network plasticity. This multidisciplinary approach builds on my broader expertise in AI-driven molecular imaging, MRI, and advanced biomedical data processing. With expertise in applied physics, medical imaging, and machine learning, I aim to transform diagnostic and therapeutic strategies.

Student roles:
The student’s role will be to support the creation of a computational framework for studying Alzheimer’s disease mechanisms and to connect this framework with experimental observations from human-derived cerebral organoids. A major responsibility will be to build and apply in silico models that capture selected disease-relevant processes, such as amyloid-β imbalance, tau-related changes, inflammatory signaling, synaptic vulnerability, and altered neuronal activity. They will also contribute to the wet-lab component of the project by helping assess whether computational predictions are supported in human iPSC-derived cerebral organoids. This may involve participation in aseptic culture procedures, organoid maintenance, disease-relevant treatments, sample preparation, immunostaining, imaging, molecular assays, calcium activity measurements, or multi-electrode array analyses. The experimental findings will be used to evaluate the accuracy of the computational model and guide subsequent refinements.
Through this role, students will act as a link between quantitative modeling and human experimental neuroscience. They will be expected to maintain well-documented code and experimental records, communicate results clearly, and collaborate with each other and other team members throughout the project. Students will gain practical experience in predictive disease modeling, cerebral organoid-based Alzheimer’s research, biological data analysis, and the integration of computational predictions with experimental testing. Weekly project meetings and mentorship will ensure the student’s progress and the success of the research.

Skills required:
The student will contribute to the development of a computationally driven Alzheimer’s disease modeling platform that integrates in silico prediction with experimental validation in human-derived cerebral organoids. The project is best suited for, but not limited to, a student with a background in biomedical engineering, computer science, computational biology, neuroscience, bioinformatics, physics, biology and relevant fields. The student should be comfortable working with pre-existing experimental or publicly available datasets and developing reproducible computational pipelines for data preprocessing, feature extraction, visualization, and model evaluation. No wet-lab experience is required for the core internship. All training will be provided.

Research area: This project lies at the intersection of multimedia systems, computer networking, distributed systems, artificial intelligence, and observability engineering. The research focuses on understanding, diagnosing, and automatically resolving performance issues in large-scale video streaming platforms. The project investigates how AI-driven analytics, distributed tracing, cross-layer telemetry, and causal inference can be used to improve Quality of Experience (QoE) for users while enabling autonomous and self-healing streaming infrastructures.

Student roles:
The student will participate in all phases of the research project under the supervision of the research team at Concordia University.

Activities may include:

• Conducting literature reviews on multimedia systems, networking, observability, and AI-based root cause analysis.
• Studying state-of-the-art video streaming architectures and Quality of Experience (QoE) metrics.
• Analyzing large-scale telemetry traces and streaming datasets.
• Developing software tools for data collection, monitoring, and visualization.
• Implementing machine learning and graph-based analytics algorithms.
• Designing and evaluating root cause analysis techniques.
• Investigating automated remediation strategies for improving streaming performance.
• Conducting experiments using real-world datasets and network emulation environments.
• Preparing technical reports, presentations, and research publications.

Students will gain hands-on experience in cutting-edge research involving AI, networking, distributed systems, cloud computing, multimedia streaming, and large-scale data analytics.

Skills required:
Strong background in computer science, software engineering, computer engineering, or a related discipline. Knowledge of programming (Python preferred), data analysis, machine learning fundamentals, networking concepts (TCP/IP), and operating systems is desirable. Experience with Linux, distributed systems, cloud computing, multimedia systems, or artificial intelligence is an asset but not mandatory. Students should be motivated to conduct research, read scientific literature, and develop software prototypes.

Research area: My research focuses on artificial intelligence, large language models (LLMs), knowledge engineering, sustainability analytics, intelligent decision-support systems, and process design and optimization. We develop human-centric AI platforms that combine machine learning, retrieval-augmented generation, sustainability assessment, and predictive analytics to support complex industrial decision-making.

Student roles:
The student will participate in the development of an AI-powered decision-support platform by integrating databases, implementing retrieval and reasoning pipelines, evaluating LLM performance, and testing recommendation capabilities. Activities include prompt engineering, data integration, model evaluation, user testing, and performance benchmarking. The student will collaborate with researchers and domain experts to validate the LLM-enabled decision intelligence platform's operability and ensure its trustworthiness.

Skills required:
Students should have a background in computer science, artificial intelligence, data science, chemical engineering, industrial engineering, or related fields. Experience with Python, machine learning, databases, natural language processing, generative AI, or cloud technologies is desirable. Interest in sustainability and decision-support applications is highly valued.

Research area: Machine learning for energy systems, reinforcement learning for EV charging optimization, demand prediction, personalized charging recommendations, and exploration of quantum computing algorithms for combinatorial optimization in smart grid contexts.

Student roles:
Weeks 1–3: Literature Review & Data Preparation
• Survey ML approaches for EV charging optimization and quantum computing applications in energy systems
• Acquire and preprocess public EV datasets
• Define optimization problem formulations for both classical and quantum approaches
Weeks 4–6: Classical ML Model Development
• Train LSTM/Transformer models for demand forecasting
• Implement reinforcement learning for charging scheduling
• Build collaborative filtering recommendation engine for personalized charging
Weeks 6–8: Quantum Algorithm Prototyping
• Formulate EV charging scheduling as a QUBO (Quadratic Unconstrained Binary Optimization) problem
• Implement QAOA and VQE circuits using Qiskit on simulated quantum hardware
• Evaluate quantum algorithm performance and scalability limits
Weeks 9–11: Live Testbed Validation
• Integrate ML models with live Secure EV testbed data pipeline
• Retrain and fine-tune models on real charging session data
• Conduct comparative analysis: classical ML vs. quantum optimization
Week 12: Dissemination
• Prepare technical report and open-source code release
• Contribute to joint research publication

Skills required:
Academic Background: Computer Science, Data Science, AI/ML, Physics (quantum), or related field. Python, machine learning fundamentals, experience with time-series data, reinforcement learning, quantum computing basics (Qiskit or Cirq), recommendation systems.

Research area: My research focuses on digital health, mobile and wearable computing, human-computer interaction, and artificial intelligence. I develop systems that combine smartphone and wearable sensor data with machine learning and large language models to support personalized health monitoring, behavior change, and health decision-making. My work emphasizes user-centered design, explainable AI, and practical health technologies that can be deployed on everyday devices.

Student roles:
Students will work as part of an interdisciplinary research team investigating how artificial intelligence can support personalized health coaching using data from smartphones, wearable devices, and personal journaling. Their responsibilities may include collecting and processing sensor data, analyzing behavioral datasets, developing machine learning models, designing personalized recommendation and nudging strategies, and evaluating system performance.

Depending on their interests and experience, students may also contribute to mobile application development, user interface design, data visualization, literature reviews, and the implementation of generative AI techniques. Students will participate in regular research meetings, present their progress, and collaborate with graduate students and faculty members throughout the internship.

A major focus of the project will be understanding relationships between everyday behaviors and health outcomes. Students may investigate how factors such as sleep, physical activity, screen time, and self-reported mood interact over time, and how these insights can be translated into timely, personalized health nudges. They will gain experience working with real-world mobile and wearable datasets and may help design and evaluate prototype systems that provide personalized feedback to users.

The project emphasizes research, experimentation, and independent problem solving. Students will be encouraged to explore new technologies, learn unfamiliar tools when needed, read relevant research literature, and contribute ideas to the design and evaluation of the system. By the end of the internship, students will have gained hands-on experience conducting research at the intersection of artificial intelligence, mobile computing, digital health, and human-computer interaction, while contributing to technologies that have the potential to improve health and well-being at scale.

Skills required:
Applicants should have a background in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field. Experience with programming in Python is required. Familiarity with machine learning, data analysis, mobile application development, wearable technologies, or data visualization is beneficial but not required.

More importantly, I am looking for students who are adaptable, and comfortable learning independently. Successful applicants should be willing to explore new technologies, read technical documentation and research papers, and quickly acquire skills needed for unfamiliar tools and frameworks. Strong problem-solving abilities and the ability to work independently and as part of a team are valuable.

Research area: Our lab researches high-fidelity distributed interaction, with both real and virtual data, to support the most demanding applications, regardless of sensor and bandwidth limitations. We work with audio, video, and haptic technologies, mixed reality and mobile computing, building systems that leverage their capabilities to facilitate and enrich both human-computer and computer-mediated human-human interaction. Active projects include development of conversational avatars for therapy and engagement with seniors, rendering audio-haptic experiences of graphics contents for users who are blind, multimodal immersive walking experiences, telepresence for music and social interaction, and design of the flight deck of the future.

Student roles:
The student will be involved in one or more of the following activities, based on a combination of identified priorities and skill set:

1. Prompt engineering to direct the large language model in carrying out its intended activity with a maximum level of proficiency to meet user expectations.
2. Integration of the system with databases from which information necessary for responses to user queries can be extracted, and/or to which user input needs to be saved.
3. Engineering the architectural support to modify conversational behaviour so as to take on new conversational tasks as requested by nurses.

Details as to these tasks will be refined according to the present state of system development nearer the time of the internship start date, and the priorities expressed by our partners in the health care sector.

Skills required:
The student should have strong programming skills, calling APIs of third-party software. Experience with generative AI, speech recognition, speech synthesis, computer vision, graphics animation, or prompt engineering would all be considered as beneficial but not imperative background.

Research area: Artificial Intelligence (AI), Cyber-security, Internet of Things (IoT), Wireless communications networks

Student roles:
The student will propose an architecture for the system and undertake the different design and programming tasks for both IoT devices and cyber-security algorithms.

Skills required:
programming skills.
wireless communication networks.
Computer networks
microprocessors
Cyber-security

Research area: My long-term research objective focuses on intra-operative target tracking for radiotherapy. In fact, real-time assessment and feedback enable clinicians to make necessary adjustments and improve the outcome of the procedure. I have a decade of experience in biomedical computing and medical devices product research, development and management. Before becoming an assistant professor at Bishop’s University and then at Toronto Metropolitan University, I used to work for a leading company in the radiotherapy industry. My research goals focused on organ tracking using medical images.

Student roles:
Develop and implement methods.
Apply the methods on datasets.
Analyze and document results.
Write conference/ journal paper.

Skills required:
Knowledge of Python.
Strong logic and fundamentals in statistics

Research area: My research develops secure, accessible, and sustainable AIoT and UAV‑based cyber‑physical systems for industrial automation and emergency services. I integrate AIoT sensing, autonomous UAV platforms, computer security, and quantum‑inspired optimization to advance technologies that enhance environmental monitoring, operational safety, and digital accessibility. Current research directions include: -AIoT‑UAV enabled smart farming and emergency response -AIoT and quantum‑inspired human‑technology interaction for accessibility -Preserving privacy from AI assistants -Decentralized Web3 solutions for resilient and transparent networks My work emphasizes practical, real‑world impact while training students in AIoT, UAV systems, cybersecurity, and emerging decentralized technologies.

Student roles:
This project aims to create a IoT-UAV based dataset of crop images from two distinct climate zones to enable comparative environmental analysis. Using this dataset, machine-learning models will be developed to detect early crop stress signs. Both traditional vegetation indices and modern deep-learning methods will be evaluated for effectiveness. The final goal is to build a prototype decision-support dashboard that converts model outputs into actionable insights for farmers.

The process starts with UAV image and sensor data preprocessing, including orthomosaic generation and radiometric correction to ensure consistent image quality. Key features such as NDVI, thermal gradients, and texture indicators will be extracted from these images. Model development will involve training classical algorithms like Random Forests and deep-learning models such as CNNs. Cross-climate validation will assess model robustness across environments. The project will conclude with a prototype dashboard built using Python and Streamlit to visualize predictions and aid decision-making.

The project will deliver a validated machine-learning model for early crop-stress detection, supporting proactive agricultural management. It will also produce a cross-climate UAV imagery dataset valuable for future research. Additionally, a functional prototype dashboard will demonstrate how model outputs integrate into farmer-facing tools. The final deliverable includes a conference-ready research poster summarizing the findings.

The intern will gain hands-on experience in UAV data processing, machine-learning model development, and agricultural analytics. Responsibilities include dataset preparation, feature extraction, model training and tuning, and dashboard development. Weekly supervision meetings will guide technical challenges, research methods, and documentation, ensuring steady progress and skill growth throughout the project.

Skills required:
The intern should have strong Python skills with machine learning, including experience with NumPy, Pandas, and Scikit‑learn, with TensorFlow or PyTorch as a plus. They should be familiar with Arduino and IoT sensors. Experience with drone or remote‑sensing imagery and calculating vegetation indices is helpful. The intern should be comfortable integrating environmental and sensor datasets. Knowledge of crops or precision agriculture is an asset but not required. Strong problem‑solving skills, attention to detail, and the ability to clearly document and communicate results are important for this project.

Research area: Dr. Subramanian's research focuses on Multi-Agent Reinforcement Learning (MARL), a subfield of Artificial Intelligence where multiple autonomous agents learn to make decisions by interacting with each other and their environment. Real-world problems like wildfire fighting, autonomous driving, and materials discovery naturally involve many interacting decision-makers, making MARL a critical tool for tackling these challenges. My work addresses two core limitations of existing MARL algorithms: poor sample efficiency (algorithms require too much data to learn) and poor scalability (algorithms become intractable with many agents). I develop principled algorithms that overcome these limitations and deploy them in real-world settings.

Student roles:
The intern will play an active and hands-on role in advancing research on transfer learning in multi-agent reinforcement learning (MARL). Under the close supervision of the principal investigator and in collaboration with graduate students in the lab, the intern will contribute to all stages of the research process, from literature review and algorithm implementation to experimentation and result analysis.

In the first phase of the internship (weeks 1–3), the intern will conduct a focused literature review on transfer learning methods in reinforcement learning, with particular emphasis on value function transfer and advisor-based learning. The intern will also set up the necessary software infrastructure, including installing and configuring relevant MARL libraries and simulation environments, and reproducing results from key baseline algorithms.

In the second phase (weeks 4–8), the intern will implement the confidence-aware value transfer framework. This involves designing and coding similarity metrics between source and target task environments, integrating the advisor's pre-trained value estimates into existing MARL algorithms, and developing the confidence scoring mechanism that determines how much the learning agent should rely on the advisor's knowledge. The intern will run initial experiments to validate the correctness of the implementation.

In the third phase (weeks 9–12), the intern will conduct a systematic empirical evaluation of the proposed framework across multiple cooperative multi-agent benchmark environments. The intern will compare performance against relevant baselines, analyze results, produce clear visualizations, and document findings. The intern is expected to contribute to a written research report summarizing the methodology, experiments, and conclusions, which may form the basis of a future research publication.

Throughout the internship, the intern will participate in weekly lab meetings, present their progress regularly, and collaborate closely with the research team. This experience will provide the intern with valuable exposure to the full research pipeline in a leading AI research lab.

Skills required:
Applicants should have a strong foundation in machine learning and linear algebra, with hands-on programming experience in Python. Familiarity with deep learning frameworks such as PyTorch or TensorFlow is expected. Prior exposure to reinforcement learning concepts (e.g., Q-learning, policy gradients, value functions) through coursework or independent study is highly desirable. Experience with scientific computing libraries such as NumPy and Matplotlib is required. Familiarity with multi-agent systems or transfer learning is an asset but not required. Strong analytical and problem-solving skills are essential, as the project involves both algorithm implementation and empirical evaluation of research ideas.

Research area: Software Programming, Cloud Computing, Embedded and real-time Systems, Internet of Things, Machine Learning

Student roles:
The student will contribute to developing the intelligent AI and decision-making components of the platform. The work will begin with the evaluation and benchmarking of machine-learning and computer-vision models suitable for deployment on IoT and edge devices.

The student will deploy, test, and compare models with different computational characteristics, measuring accuracy, latency, resource consumption, and scalability. They may investigate optimization techniques such as quantization, pruning, model conversion, or hardware acceleration to improve execution efficiency.

Using these performance measurements, the student will design decision-making frameworks based on reinforcement learning and multi-agent systems. Device-side agents and edge-side agents will be modeled as cooperating entities that observe local system conditions and make decisions regarding model selection, task execution, resource allocation, and workload distribution.

The student will implement and evaluate reinforcement-learning algorithms and compare them against baseline approaches such as static model selection, rule-based strategies, local-only execution, and edge-only execution. Performance metrics may include latency, accuracy, energy consumption, resource utilization, and overall quality of service.

Expected outcomes include model benchmarking tools, optimized AI models, reinforcement-learning environments, training and evaluation pipelines, learned decision policies, comparative performance analyses, and recommendations for integrating intelligent orchestration into the IoT–edge platform.

Skills required:
Strong Python programming
Machine Learning and Deep Learning fundamentals
PyTorch and/or TensorFlow
Computer Vision (OpenCV, object detection, classification)
AI model evaluation and benchmarking
Reinforcement Learning fundamentals
Multi-agent systems concepts
Data analysis and experimentation
Basics of Linux and Git
Scientific reporting and technical documentation

Research area: My specialized research area is computer networks and communications, with a focus on software-defined networking (SDN), satellite communications, network optimization, cybersecurity, and intelligent network management. My research investigates the design of scalable, resilient, and efficient network architectures for next-generation communication systems. A key area of interest is Software-Defined Satellite Networks (SDSNs), where I develop optimization and machine learning techniques to improve network performance, resource utilization, and control-plane efficiency. My work also explores network security, intrusion detection, and emerging technologies that enhance the reliability and adaptability of modern terrestrial and space-based communication infrastructures.

Student roles:
The student will contribute to a 12-week research project focused on developing and evaluating a clustering-based framework for controller placement in Software-Defined Satellite Networks (SDSNs). The work will begin with a targeted literature review on SDN controller placement, satellite network architectures, and clustering-based optimization techniques to understand existing approaches and identify key challenges.

The main technical task for the student is to design and implement a clustering algorithm that partitions a large satellite network into smaller, manageable clusters based on characteristics such as network connectivity, latency, and topology. This clustering step is central to the project, as it reduces the complexity of the controller placement problem by restricting optimization within each cluster rather than across the entire satellite network. After cluster formation, the student will implement controller placement strategies within each cluster to improve scalability, reduce latency, and enhance load balancing.

The student will also develop simulation models using network simulation tools such as OMNeT++ or equivalent platforms. These simulations will be used to evaluate the proposed framework under different network scenarios and conditions. Performance metrics including controller-to-satellite latency, communication overhead, controller workload distribution, scalability, and fault tolerance will be analyzed.

In addition, the student will compare the proposed cluster-based approach with baseline controller placement methods to demonstrate its advantages. The student may also explore basic optimization or machine learning techniques to improve clustering quality and controller selection, depending on progress within the 12-week timeline.

Throughout the project, the student will document all methodologies, simulation setups, and results to ensure reproducibility. The student will prepare a final technical report summarizing the research outcomes and may contribute to a conference or journal paper if results are suitable for publication.

Overall, the student’s role is focused on algorithm development, simulation, performance evaluation, and research documentation, providing practical experience in software-defined satellite networks.

Skills required:
The ideal student should have a strong background in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Knowledge of computer networks, communication systems, and network protocols is essential. Familiarity with Software-Defined Networking (SDN), satellite communications, network optimization, or graph theory is desirable. The student should possess programming skills and have experience with simulation and data analysis. Experience with network simulation tools such as OMNeT++, NS-3, OPNET, or similar platforms is highly preferred. Strong analytical, problem-solving, research, and communication skills are required.

Research area: This research lies at the intersection of Machine Learning, Software Engineering, and Artificial Intelligence Operations (AIOps). The project focuses on concept drift detection and adaptive model retraining in large-scale Continuous Integration and Continuous Deployment (CI/CD) environments. The research investigates how machine learning models degrade over time as software systems evolve and explores automated techniques to identify distribution shifts, determine optimal retraining windows, and maintain model effectiveness while minimizing computational costs.

Student roles:
The student will work as a research intern within a collaborative AI and Software Engineering research team. Their primary responsibility will be to investigate concept drift in machine learning models and evaluate adaptive retraining strategies using real-world CI/CD datasets.

The student will conduct literature reviews on concept drift detection, online learning, and MLOps practices. They will assist in preparing datasets, implementing drift detection algorithms, conducting experiments, and analyzing results. The student will compare various approaches for identifying data distribution changes and determine when retraining should occur to maximize model effectiveness.

Additional responsibilities include developing experimental pipelines in Python, performing statistical analyses, creating visualizations, documenting findings, and contributing to research reports and publications. The student will participate in weekly research meetings, present progress updates, and collaborate with faculty members and graduate researchers.

By the end of the internship, the student will gain hands-on experience with machine learning research, large-scale data analysis, software engineering datasets, concept drift detection, and AI model lifecycle management. Strong candidates may have opportunities to contribute to conference or journal publications arising from the project.

Skills required:
Applicants should have completed undergraduate coursework in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related discipline. Experience with Python programming and machine learning fundamentals is required. Familiarity with data analysis, statistics, Git, and software development practices is desirable. Knowledge of machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch is considered an asset. Previous exposure to software testing, CI/CD systems, or MLOps concepts is beneficial but not mandatory.

Research area: Since July 2021, Nadia Tahiri has held the position of Assistant Professor in the Department of Computer Science at the Université de Sherbrooke. Her research program primarily focuses on enhancing our understanding of the mechanisms responsible for biodiversity generation and maintenance. Specifically, she is dedicated to identifying evolutionary processes and developing new criteria for this purpose. Throughout her career, she has undertaken various projects, including the creation of a rapid and reliable method for detecting and confirming horizontal gene transfer events using phylogenetic trees.

Student roles:
As part of this internship, students will have the opportunity to delve into the development of a novel phylogenetic tree metric. Among the potential options, the choice may be made between metrics such as Quartet distance or least-squares distance. The students will undertake the implementation of an efficient data structure, focusing on seamless data insertion, deletion, and search operations. Subsequently, they will collaborate with master's and doctoral students to integrate these metrics into the lab's existing algorithms, namely Tahiri et al. 2018 and Tahiri et al. 2022. As a culminating step, the students will engage in the analysis of real-world data, working closely with the research team to gain valuable insights.

Skills required:
The ideal candidate should possess strong programming skills in languages such as C, C++, Python, as well as proficiency in web languages like CSS and JavaScript. Knowledge of algorithms and graph theory is desirable. Familiarity with bioinformatics would be an added advantage. The student should be highly independent, proactive, and able to work autonomously. Fluency in both written and spoken English or French is required.

Research area: Our research integrates advanced sensing technologies, LiDAR mapping, computer vision, and machine learning to automate the detection, classification, and analysis of transportation infrastructure. A major focus of our work is the development of annotated geospatial datasets and AI models for semantic segmentation and object detection of road and roadside features. These technologies support infrastructure inventory creation, accessibility assessment, curbside management, digital twin development, and evidence-based transportation planning.

Student roles:
We are seeking a research intern to support the development of AI-enabled transportation infrastructure analysis tools. Responsibilities will include:
1. Assisting with the annotation, quality control, and management of large-scale LiDAR and geospatial datasets used for machine learning applications.
2. Developing, training, and evaluating semantic segmentation and object detection models for identifying transportation assets and roadside features.
3. Supporting the extraction of quantitative roadway and curbside characteristics, including parking infrastructure, accessibility features, roadway geometry, slopes, and surface conditions.
4. Integrating segmentation and object detection outputs into automated transportation analysis and reporting workflows.
5. Supporting the development of LLM-based scene description and automated report-generation tools that translate AI-derived outputs into actionable transportation insights.
6. Preparing technical documentation, visualizations, and reproducible workflows for future deployment and research dissemination.

An average day will involve data annotation, preprocessing point clouds, training AI models, evaluating segmentation outputs, conducting transportation analyses, and participating in weekly meetings with graduate students and project supervisors.

Skills required:
Foundational programming knowledge, preferably in Python, with interest in applying machine learning tools.
Strong written communication skills, particularly in academic or technical contexts.
Introductory exposure to 3D data (point clouds) or a demonstrated interest in working with spatial datasets.

Highly Desirable:
Interest in transportation applications of AI, LiDAR analytics, and geospatial data science.

Considered an Asset:
Any experience with ROS, sensor integration, or SLAM.
Familiarity with GIS platforms and object detection techniques.
Experience with annotation, point cloud labeling, object detection, or semantic segmentation.
Familiarity with machine learning frameworks (PyTorch, TensorFlow, related computer vision libraries).

Research area: My specialized research area is the design and analysis of machine learning-enhanced metaheuristic optimization algorithms, with a particular focus on reinforcement learning for adaptive control of population-based and restart-based search methods. My work explores how optimization algorithms such as Particle Swarm Optimization, Differential Evolution, and CMA-ES can learn to adjust their parameters, operators, restart strategies, and search behaviour in response to the characteristics of the problem being solved. More broadly, my research lies at the intersection of evolutionary computation, swarm intelligence, reinforcement learning, and black-box global optimization.

Student roles:
The student will contribute to the development and evaluation of reinforcement-learning-enhanced metaheuristic optimization algorithms. Their role will include becoming familiar with the existing research codebase, reviewing relevant literature, helping identify a suitable advanced reinforcement learning direction, and implementing selected extensions to the current framework.

The student will run computational experiments on benchmark optimization problems, compare the results against baseline algorithms or simpler reinforcement learning approaches, and help analyze the behaviour of the learned agents. They will also be expected to organize experimental results, prepare figures or summaries, document their code, and contribute to a final report. If the results are promising, the student may also contribute to the preparation of a research paper or conference submission.

Skills required:
The student should have a strong interest in artificial intelligence, machine learning, and optimization. Good programming skills in Python are required, including the ability to understand and modify existing research code. Strong background in machine/deep learning is expected, and familiarity with reinforcement learning, evolutionary computation, or metaheuristic optimization would be an asset. Experience with libraries such as PyTorch, TensorFlow, NumPy, pandas, or similar scientific computing tools would also be useful.

Research area: Interdisciplinary areas of applied electromagnetics and wireless communications, with a particular focus on the development of high-performance computational models/algorithms for emerging wireless technologies in 5G/6G/THz wireless communications, intelligent transportation (air, ground, underground), underwater communications, industrial Internet of Things, as well as biomedical sensing and healthcare applications. Keywords: Applied Electromagnetics, 5G/6G Wireless, Integrated Sensing & Communication, Localization, Antennas & RF/Microwave Design, Machine Learning & Parametric Modeling, Stochastic Uncertainty Quantification, Internet of Things & Intelligent Systems.

Student roles:
(1) Assisting in conducting literature reviews to gather relevant research papers, articles, and other academic resources to support the research project.
(2) Collaborating with the research team to exchange ideas, share knowledge, and contribute to the overall research goals, including methodology & model development, results analysis, etc.
(3) Participating in group meetings, and effectively communicating project progress and results.
(4) Keeping records of research procedures, observations, and findings. Writing reports, summaries, and contributing to scientific papers writing or presentations.

Skills required:
The project suits students with interests and backgrounds in mathematics, electromagnetics, wireless communications, or machine learning and programming.

Research area: My research area is optimization with specific interest in designing least expensive roads that satisfy safety, environmental, and engineering constraints. My collaborators and I have been working for more than 15 years with a company that sells road design software. We have modelled, implemented, and tested a complete set of models that allow to optimize an entire road network with guarantees that no other solution would be less expensive. This multi-year project has been a valuable application of my core knowledge in optimization and convex analysis.

Student roles:
The project will start by understanding the requirements including specific engineering constraints. It will then proceed to identifying promising algorithms, implementing several, and comparing their performance. The goal is to recommend to our industry partner a fast accurate algorithm that is explainable to civil engineers. AI tools may be used to speed up coding, e.g., generating unit tests, or find promising algorithms from the literature. The typical work environment include access to powerful workstations (dual-CPU dual GPU) with more computational power available if required.

Skills required:
The student should have some background in coding and mathematics with an interest in 2D geometry. Specific optimization techniques will be taught when needed. A familiarity with AI to speed up coding would be useful, but is not required. Likewise, knowledge of how to compare algorithm results numerically would be useful, but can be taught as the project unfolds. The student should have acceptable communication skills, and be able to work independently.

Research area: My research focus lies in the field of Mathematical Optimization, the study of minimizing or maximizing a function. The focus of this project is derivative-free optimization, or the study of the mathematical study of optimization algorithms that do not use derivatives.

Student roles:
The student will explore Mathematical theory, development novel approximation techniques, and work on Mathematical proofs of convergence.
The student may implement and test ideas in software.

Skills required:
Students should be strong in multivariate calculus and numerical analysis, particularly Taylor's Theorem. Students should be comfortable with software implementation in MATLAB or Python. Recommended reading "Derivative-free and Blackbox Optimization" by Audet and Hare (2017).

Research area: This project focuses on using artificial intelligence and machine learning to support the inverse design of sustainable soilless growing media (SGM). The goal is to develop data-driven tools that can suggest suitable media recipes based on desired properties such as total porosity, container capacity, air space, bulk density, pH, and EC. The work will build on existing experimental datasets to move toward recipe recommendation. The project connects sustainable agriculture, compost-based circular economy practices, and AI to reduce trial-and-error in growing media formulation.

Student roles:
The undergraduate student will support the development and testing of an AI-driven inverse design approach for sustainable SGM. This role is suitable for a student interested in applying data science, machine learning, and simple user-focused design ideas to a real agricultural research problem.
The student will assist with testing recipe recommendation approaches where desired target properties, such as total porosity, container capacity, air space, bulk density, pH, and EC, are used to suggest possible SGM recipes. They will help compare recommended recipes against the target properties and summarize whether the suggested formulations are practical and meaningful. This will help evaluate how well the inverse design approach can reduce trial-and-error in growing media formulation.
The student may also support the early planning of a future web or app-based decision-support interface. Their role in this part will be to help review how recommended recipes, predicted properties, and model outputs can be presented clearly and effectively for future users, including researchers, industry professionals, farmers, and growers.
Throughout the project, the student will prepare clear summaries of their work using tables, figures, and short progress updates. They will participate in regular research discussions, receive guidance from the graduate research team, and contribute to improving how AI-based recipe recommendations can be tested and communicated.
This role will provide the undergraduate student with valuable interdisciplinary experience in sustainable agriculture, applied machine learning, inverse design, and practical decision-support tool development.

Skills required:
The ideal candidate will be a motivated and curious student who is interested in applying artificial intelligence and data-driven tools to real-world challenges in sustainable agriculture. A background in computer engineering, computer science, artificial intelligence, data science, environmental engineering, or a related field would be suitable for this project.
Basic Python programming experience will be helpful, especially for data handling, machine learning model testing, and result visualization using tools such as Pandas, NumPy, scikit-learn, Matplotlib, or similar libraries. Familiarity with machine learning concepts.

Research area: I study the human faculties of beat perception, rhythm production, and sensorimotor synchronization through the lens of computational modelling. I am interested in relationships between beat perception, physical movement, the vestibular system, and speech, particularly during development. I build models that illustrate and explore the roles of specific brain structures, computational principles, or developmental learning processes.

Student roles:
Student will engage in a loop of writing new code for the model, training it, and testing it; they will visualize and quantify results; they will brainstorm approaches to complex rhythm learning; and they will write up a summary of findings.

Skills required:
Proficiency with neural network training, ideally in PyTorch
Interest in neuroscience and music
At least an intermediate level of instrument/voice/dance training

Research area: My research area is at the intersection of eXtended Reality (XR) and Human-Computer Interaction (HCI). XR includes Virtual, Mixed, and Augmented Reality (VR, MR, and AR, correspondingly). Students in my lab develop novel solutions for XR systems that are applied in areas such as medicine, for instance in the Neonatal Resuscitation Training (NRP). We also explore research in integrating smartphones with Virtual Reality. In particular we have developed methods to authenticate users in VR using smartphones. I also collaborate with researchers in Pscyhology, where we create virtual environments to evaluate psychological theories, such as the effects of weapons on memory.

Student roles:
The student will support the research and development activities of the 3D Telepresence and Telecollaboration Laboratory and join a team of graduate students who are doing a variety of research projects. The student will become familiar with the different software prototypes that are implemented in the lab for our research project. The student will assist in the development of software for XR systems developed in the lab and be responsible for the conceptualization of the solution to the problems presented in the description of the research project. The student will be supported by the graduate students in the lab and will also be helping graduate students to run other user studies. There will be regular meetings with the supervisor, as well as with other members of the lab. Students who participate significantly in the development of work that is published in conference papers or journal articles will also get acknowledged as co-authors in the papers. While the focus will be on the MR-NRP, it will be possible for the student to explore participating in other research projects of the lab, such as the VR Forensic Lab, and the research on the use of smartphones for authentication in VR, as well as the use of smartphones in XR in general.

Skills required:
The interested student should have a general interest in Researching and developing Interactive Graphics Systems, implementing and working with software that enables users to experience Immersive Virtual environments. Good programming skills in C#/C++/Python would be helpful for this project. The student should also have a good grasp and interest to learn what it means to do a user study, where a software prototype that has been developed is evaluated against competing implementations. Ability to use game engines such as Unity, Unreal, and Godot, would be an important asset. Having mathematical, spatial, geometrical and structured thinking skills is an asset too.

Research area: My research focuses on artificial intelligence, large language models, affective computing, human–AI interaction, and trustworthy AI. I study how AI systems interpret, generate, and adapt emotional and social signals in human-facing contexts, particularly in education, health communication, and public digital support. My work combines machine learning, natural language processing, user modeling, evaluation of LLM-generated responses, and human-centred AI design. This project builds on my expertise in emotion-aware AI, responsible LLM use, and the design of safe, explainable, and context-sensitive AI systems.

Student roles:
The intern will contribute to all stages of the 12-week research project under close faculty supervision. In the first stage, the student will review selected literature on LLM agents, affect-aware computing, human–AI interaction, and safe AI response generation. The intern will help define the project scope, identify relevant affective and contextual cues, and develop a synthetic scenario set for evaluation.
In the second stage, the intern will design a response-policy taxonomy for agentic AI. This taxonomy will specify when an AI agent should answer directly, provide reassurance, ask a clarifying question, issue a caution, or recommend hand-off to a human or qualified professional. The student will then implement a proof-of-concept workflow in Python using structured prompts, LLM-based agent components, and simple evaluation scripts.
In the final stage, the intern will test the prototype on synthetic scenarios, analyze successful and failed cases, and document limitations. The student will prepare summary tables, visualizations, and evaluation results, contribute to the final technical report, and present the project outcomes. The intern will not work with real users, sensitive personal data, clinical decision-making, surveillance, or production systems. The project is designed as a focused and feasible undergraduate research internship with clear weekly milestones, regular supervision, and concrete outputs suitable for future academic publication or graduate-level extension.

Skills required:
The ideal intern should have a background in computer science, artificial intelligence, data science, software engineering, or a related field. Required skills include Python programming, basic machine learning or natural language processing, ability to read research papers, and clear written communication. Experience with large language models, prompt engineering, APIs, data annotation, evaluation metrics, or human–computer interaction is an asset but not required. The student should be careful, organized, intellectually curious, and interested in responsible AI, affect-aware computing, and the evaluation of AI systems in human-facing contexts.

Research area: Smart environments increasingly combine sensors, connected devices, data streams, spatial interfaces, and AI systems. As these environments become more complex, people need better ways to understand what is happening, express intentions, receive explanations, and maintain appropriate control. This research area explores agentic AI interfaces for smart environments, focusing on human-AI interaction, context-aware computing, intelligent assistants, Internet-of-Things systems, multimodal interfaces, and responsible human-in-the-loop design. Rather than treating smart environments only as collections of devices, this work investigates AI agents as an interaction layer between people, data, services, and physical spaces.

Student roles:
The student will help design and develop an agentic AI interface for a smart-environment scenario. The student will begin by reviewing relevant literature and tools related to AI assistants, agentic systems, smart environments, context-aware computing, and human-AI interaction. Based on this review, the student will help define a focused prototype scenario suitable for a 12-week research internship.

The student will implement a small prototype showing how an AI agent or assistant can help a user understand or interact with a smart environment. The prototype may involve simulated or real sensor data, connected devices, environmental states, user goals, or contextual information. The interface may be implemented as a web application, conversational assistant, dashboard, spatial interface, or other interactive prototype depending on the project direction and the student’s skills.

The student will investigate how users can express intentions to the system, how the agent should respond, what information should be shown, and how the system can preserve human oversight. Where feasible, the student will conduct a small-scale evaluation such as through informal analytics or user testing, expert walkthroughs, task-based evaluation, or qualitative feedback from lab members. The student will document design decisions, technical implementation details, limitations, and future research directions, and may contribute to demos, posters, workshops, technical reports, or publications.

Skills required:
An optimal skillset includes a background in computer science, human-computer interaction, artificial intelligence, digital media, software engineering, or a related field; programming experience in Python, JavaScript, TypeScript, Java, C#, C/C++, or similar languages; interest in AI assistants, large language models, agentic systems, human-AI interaction, or context-aware computing; familiarity with web development, APIs, dashboards, chat interfaces, or prototype development; and interest in AI interaction, usability evaluation, explainable AI, or human-centered design. Experience with IoT, sensors, Raspberry Pi, Arduino, Unity, or WebXR is valuable.

Research area: “GeoResourceCloud” is a research program (https://www.georesourcecloud.com) that focus on developing a multi-scale, multi-physics and data-driven decision makings platform for carbon capture, utilization and storage (CCUS), hydrogen storage,enhanced geothermal system and broad Geo-energy and GeoResource industry. As a researcher in this project, you will join a multi-disciplinary and vibrant research group (https://www.georesourcecloud.com/team), collaborate with our industrial and academic partners to achieve a variety of milestones, and cultivate your skillset to prepare yourself for future challenges during energy transition.

Student roles:
The student will contribute to the development and implementation of an agentic AI system for subsurface energy and resource development, with responsibilities aligned to three core components: the Geo (Resource Modeling) Agent, Production Agent, and Economy Agent. The role is designed to be flexible, allowing students to engage in different tasks depending on their background while contributing to an integrated workflow.

For the Geo (Resource Modeling) Agent, the student will focus on subsurface data processing and model construction. This includes handling geological and well log data (e.g., LAS/CSV parsing), performing geostatistical analysis such as interpolation and spatial mapping, and assisting in building reservoir or geomodel representations. The student will also support data quality control, unit consistency, and preparation of inputs for downstream simulation and analysis.

For the Production Agent, the student will work on production data analysis and forecasting. Responsibilities include organizing and querying production datasets, implementing decline curve analysis (DCA), and supporting reservoir simulation or surrogate modeling workflows. The student will help translate engineering questions into automated pipelines, ensuring results are reproducible and aligned with physical and operational constraints.

For the Economy Agent, the student will contribute to techno-economic evaluation and decision support. Tasks include linking production forecasts with economic indicators such as cost, revenue, and net present value, conducting sensitivity and scenario analyses, and assisting with optimization under uncertainty.

Across all components, the student will play a key role in workflow automation and system integration, including developing Python-based tools, interfacing with databases, and supporting agent orchestration. Additional responsibilities include validating outputs, documenting workflows, and generating visualizations. The position provides hands-on experience in integrating AI, numerical modeling, and engineering for scalable subsurface applications.

Skills required:
The project is interdisciplinary and open to students from engineering and science backgrounds, including civil, petroleum, geological, mining, computer engineering, geoscience, physics, and applied mathematics. Applicants should have strong analytical and problem-solving skills, with a willingness to learn coding, data analysis, and computational modeling. Experience in Python is an asset but not required. Exposure to geological modeling, reservoir simulation, geomechanics, or techno-economic analysis is beneficial. Students interested in AI, machine learning, or digital twins can contribute to agent development, while those with traditional engineering backgrounds can focus on physics-based modeling. Adaptability and a proactive learning mindset are essential.

Research area: This project focuses on the use of the Model Context Protocol (MCP) to develop intelligent AI agents that can interact seamlessly with software engineering tools and development environments. MCP is emerging as a standardized protocol that enables AI agents to access and interact with repositories, issue trackers, CI/CD pipelines, documentation systems, and other software development resources. The project aims to investigate how MCP-enabled agents can automate and enhance software engineering activities, improve developer productivity, and support decision-making throughout the software development lifecycle.

Student roles:
The student will participate in all stages of the research project, including literature review, system design, implementation, experimentation, and evaluation. They will investigate existing MCP frameworks and identify opportunities to apply MCP-enabled agents to software engineering challenges.

The student will develop prototype AI agents capable of interacting with various software engineering tools through MCP servers. These agents may support activities such as code review, bug triaging, test generation, requirements analysis, documentation management, technical debt identification, and CI/CD monitoring.

The student will design and conduct empirical studies to evaluate the effectiveness, efficiency, and reliability of MCP-based agentic systems in realistic software development environments. They will collect and analyze data, compare different approaches, and identify factors influencing the successful adoption of AI agents in software engineering workflows.

The student will work closely with researchers and graduate students in the Software Engineering Research Laboratory at ÉTS Montréal. Responsibilities will also include analyzing experimental results, preparing technical reports, and contributing to the dissemination of research findings.

Depending on project progress, the student may have opportunities to contribute to open-source tools and co-author research papers submitted to leading software engineering and AI conferences.

Skills required:
Applicants should have a background in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline. Experience with programming languages such as Python, Java, or JavaScript is desirable. Familiarity with software development tools, Git/GitHub, APIs, cloud platforms, or software architecture concepts is beneficial. Knowledge of large language models, AI agents, software engineering practices, or distributed systems is considered an asset. Strong analytical, problem-solving, and communication skills are expected.

Research area: My research focuses on software engineering for embedded and cyber-physical systems, with emphasis on embedded software testing, hardware-in-the-loop validation, AI-assisted software engineering, edge AI, and distributed computing. I develop methods and experimental platforms to improve the reliability, maintainability, and deployment of embedded systems operating under hardware, timing, and resource constraints. My recent work explores agentic AI and large language models for software analysis, testing, debugging, and system understanding in embedded, IoT, and autonomous system contexts, with applications in industrial automation, automotive systems, and intelligent infrastructure.

Student roles:
The intern will own the design and implementation of the agentic AI testing framework from end to end, with the supervisor providing the hardware-in-the-loop bench, research direction, weekly mentorship, and writing guidance.

Weeks 1 and 2: Onboarding, literature review on agentic AI for software engineering, large language models for code understanding, automated test generation, and hardware-in-the-loop testing. The intern will produce a focused literature synthesis and a refined system architecture.

Weeks 3 and 4: Set up the development environment and the pre-configured hardware-in-the-loop bench. Build the retrieval-augmented generation pipeline grounded in firmware documentation and chip datasheets. Identify and prepare three representative open-source firmware targets from ecosystems such as Zephyr, FreeRTOS, or Arduino.

Weeks 5 and 6: Implement the agent's core tool interface, including code reading, test compilation, test deployment to the bench, and runtime trace capture. Iterate on prompt design and tool descriptions on a single firmware target.

Weeks 7 and 8: Implement the failure analysis and follow-up test generation loop. Tune the agent for coverage progression and fault detection. Build a dashboard that visualizes the agent's reasoning traces and the evolving test suite.

Weeks 9 and 10: Run the full evaluation across all three firmware targets. Compare against a human-authored baseline test suite on coverage, fault detection rate, and wall-clock time. Identify and document failure modes of the agent.

Weeks 11 and 12: Conduct statistical analysis. Draft a short paper. Prepare the open-source release including the framework, the benchmark, prompts, and reproducibility documentation.

The intern will join weekly one-on-one supervision meetings and weekly group meetings, and will present a final talk at the end of the internship.

Skills required:
Strong Python programming and prior experience with at least one large language model API such as Anthropic, OpenAI, or open-source models served through Ollama. Familiarity with retrieval-augmented generation pipelines and agentic frameworks such as LangChain or LangGraph is important. Comfort reading embedded C or C++ code is required, although writing new firmware is not necessary. Comfort with Git, Linux, and basic shell scripting is required. Coursework or projects in software testing, software engineering, or embedded systems is strongly preferred. Interest in AI-assisted software engineering research is strongly valued.

Research area: AI, ML, Systems, Programming Languages, Software Testing

Student roles:
Design, development, and reporting

Skills required:
Programming knowledge, AI/ML Knowledge

Research area: My research focuses on AI-assisted software engineering, agentic AI, and applied immersive technologies, alongside my core work in embedded and cyber-physical systems. I am interested in how large language models, retrieval-augmented generation, and multi-tool AI agents can be combined with virtual reality and sensor data to create adaptive training environments for high-risk industries. Construction is one of the highest-risk sectors for workplace injury, and immersive training that adapts to learner behaviour and real-world hazard data has strong potential to improve safety outcomes. This direction combines my interests in applied AI, real-time systems, and human-centred technology.

Student roles:
The intern will own the design and implementation of the VR training environment and the agentic AI layer from end to end, with the supervisor providing the VR hardware, research direction, weekly mentorship, ethics protocol support for the pilot study, and writing guidance.

Weeks 1 and 2: Onboarding, literature review on VR-based safety training, adaptive learning systems, agentic AI in immersive environments, and human-computer interaction methods. The intern will produce a focused literature synthesis and a refined system architecture.

Weeks 3 and 4: Build the Unity scene representing a construction site with at least three hazard categories such as fall risks, struck-by hazards, and electrical hazards. Implement learner interaction mechanics and instrumentation to capture learner actions in structured form.

Weeks 5 and 6: Implement the communication layer between the Unity scene and the Python-based agent backend. Build the agentic AI layer using the supervisor's preferred large language model API and agentic framework.

Weeks 7 and 8: Design the pedagogical adaptation strategy. Implement tools the agent can invoke to query learner performance, select the next hazard scenario, and inject instructional cues. Tune for responsiveness and pedagogical coherence.

Week 9: Implement and validate the fixed-scenario baseline used for comparison in the pilot evaluation.

Weeks 10 and 11: Run a small pilot evaluation with volunteer participants under an approved protocol. Half of participants experience the adaptive system; half experience the baseline. Collect performance and engagement data.

Week 12: Conduct statistical analysis. Draft a short paper. Prepare the open-source release including the Unity project, the agent backend, and reproducibility documentation.

The intern will join weekly one-on-one supervision meetings and weekly group meetings, and will present a final talk at the end of the internship.

Skills required:
Strong programming skills in C# and Python, with prior experience in Unity development required. Familiarity with at least one large language model API such as Anthropic, OpenAI, or open-source models served through Ollama is important. Comfort with agentic frameworks such as LangChain or LangGraph and with retrieval-augmented generation is preferred but can be learned. Comfort with Git, Linux or Windows development environments, and basic 3D scene design is required. Coursework or projects in computer graphics, human-computer interaction, or game development is strongly preferred. Interest in immersive training and applied AI research is strongly valued.

Research area: My research interests primarily lie in cloud computing, optimization, mathematical modeling, reliability analysis, reliable resource management, virtual machine (VM) placement and migration, and survivable network design. I employ a range of methodologies including mathematical modeling, optimization techniques, algorithm design, game theory, and machine learning. Currently, my work is expanding into edge computing, a critical paradigm for enabling latency-sensitive and computation-intensive applications by offloading tasks closer to end-user devices. In this project, we aim to develop an Agentic AI in Edge–Cloud for Resource Management tailored for dynamic edge environments characterized by device heterogeneity, intermittent data traffic, and varying network conditions.

Student roles:
Conduct literature reviews on Agentic AI, multi-agent systems, edge–cloud computing, task offloading, and intelligent resource management techniques.
Collect, preprocess, and analyze datasets related to workload demand, network traffic, resource utilization, and edge–cloud system performance.
Assist in developing AI-based models for workload prediction, task scheduling, and dynamic resource allocation in distributed computing environments.
Support the implementation of NLP-based task interpretation mechanisms for converting user or application requests into actionable computational tasks.
Develop and configure simulation environments for evaluating edge–cloud orchestration, task offloading, and resource management strategies.
Conduct simulations and experimental studies to evaluate system performance under varying workload, network, and resource conditions.
Analyze performance metrics such as latency, throughput, energy efficiency, scalability, cost, and quality of service.
Assist in designing and testing automated monitoring, orchestration, and task migration mechanisms for adaptive system optimization.
Contribute to software development, debugging, validation, and prototype implementation using AI, cloud, and edge computing frameworks.
Prepare technical documentation, research reports, presentations, and research publications related to the project outcomes.

Skills required:
The ideal candidate for this MITACS internship will have a solid academic background in Computer Science, Artificial Intelligence, or a closely related discipline, preferably at the undergraduate or graduate level.
Key qualifications include:
1. Strong programming skills, with a focus on Python and relevant AI/ML frameworks.
2. Experience working with large datasets and conducting data-driven research.
3. Excellent analytical and problem-solving abilities. Experience with big data technologies, AI-related research, and publications in relevant areas are considered strong assets.
4. The candidate should be capable of working both independently and collaboratively, demonstrating initiative, creativity, and a proactive approach to research challenges.

Research area: My specialized research lies in next-generation (5G/6G) wireless communications and the artificial intelligence that will make future networks autonomous. As a Canada Excellence Research Chair and Full Professor at Memorial University, and Fellow of the IEEE, IET, CAE, EIC, and AAIA, I work across quantum machine learning, signal processing, and real-time optimization, applied to digital twin networks, ISAC, integrated satellite–terrestrial networks, semantic communications, physical-layer security, IoT and smart cities. A recipient of the RAEng Research Chair and the UK Newton Prize, and Editor-in-Chief of IEEE Communications Surveys & Tutorials, I have published 400+ journal articles with 27,000+ citations(h-index 87).

Student roles:
The intern will be a hands-on member of the research team, contributing directly to the design, implementation, and evaluation of agentic AI systems for digital-twin-based network security. Working under my supervision alongside graduate students and research associates, the student will take ownership of well-scoped technical tasks while learning the full research workflow.

Concretely, the student will: (1) help build and configure containerized digital-twin sandbox environments using Docker, ensuring reproducible and isolated test conditions; (2) implement and extend agent pipelines that integrate large language models with external tools and retrieval-augmented knowledge sources; (3) prepare benchmark scenarios and run controlled experiments across multiple frontier models; (4) collect, process, and analyse results on accuracy, reproducibility, and cost, producing clear figures and summaries; and (5) document code, methods, and findings to support reproducibility and an open-source release.

Beyond these tasks, the student will participate in weekly group meetings, present progress, read and discuss relevant literature, and refine the research direction collaboratively as results emerge. They will be encouraged to take initiative, proposing improvements to the agent design, experimental setup, or evaluation metrics, and to develop independent problem-solving skills within a supportive environment.

The role is well suited to a motivated undergraduate who is comfortable with programming and the command line, eager to learn, reliable in delivering on tasks, and excited to work at the intersection of machine learning, networking, and cybersecurity.

Skills required:
We seek a motivated undergraduate in computer science or a closely related field with solid programming skills (e.g., Python and/or Java) and a strong interest in artificial intelligence and large language models. Prior hands-on experience applying or evaluating machine learning or natural language processing (NLP) models in a research setting is highly valued, as is familiarity with the Linux/Unix command line and exposure to cloud platforms (e.g., Microsoft Azure), data engineering, or containerization. An interest in computer networking or cybersecurity, strong problem-solving and communication skills, and the ability to work independently and in a team are essential.

Research area: My research focuses on topics related to software reuse, recommendation systems, big data mining in software repositories, testing for continuous system integration in cloud environments, cloud- based architecture microservices and mobile computing and IoT systems, as well as the application of artificial intelligence techniques to software engineering.

Student roles:
The intern will work alongside graduate students to:
Individually analyze each topic, prepare subsequent activities, and explore the various underlying concepts. The main topics include:
• Intelligent application systems
• Orchestration and operation of intelligent applications (MLOps)
• Limitations and constraints of standard practices
• Use cases and areas for improvement
Following this exploration, one or more proposals will be presented to describe potential solutions. These proposals will then be discussed in order to select one solution, which will subsequently be developed and evaluated

Skills required:
• Proficiency in basic software engineering concepts
• Proficiency in basic cloud computing concepts
• Proficiency in basic service-oriented architecture (SOA) concepts
• Proficiency in at least one programming language (Python, Java, C++, C#, etc.)
• Basic knowledge of machine learning concepts (Neural Networks, Clustering, Supervised Learning, etc.)

Research area: My research focuses on topics related to software reuse, recommendation systems, big data mining in software repositories, testing for continuous system integration in cloud environments, cloud- based architecture microservices and mobile computing and IoT systems, as well as the application of artificial intelligence techniques to software engineering.

Student roles:
-Conduct a literature review on Continuous Integration (CI) optimization, Agentic AI, Large Language Models (LLMs), and machine learning techniques for build prediction.
-Collect, preprocess, and analyze data from software repositories and CI pipelines to support model development and evaluation.
-Develop and evaluate machine learning, LLM-based, and Agentic AI approaches for predicting build outcomes and reducing unnecessary CI executions.
-Design and implement prototypes that integrate AI-driven decision-making into CI workflows.
-Create benchmarks and perform experiments to compare the proposed solutions against state-of-the-art approaches using relevant performance and cost-reduction metrics.
-Collaborate with the research team, document findings, and regularly present progress, results, and recommendations.

Skills required:
• Proficiency in at least one programming language (Python, Java, C++, C#, etc.).
• Basic knowledge of Machine Learning concepts (Neural Networks, Clustering, supervised learning, etc.).
• Skills in mathematics and statistics.

Research area: My research focuses on topics related to software reuse, recommendation systems, big data mining in software repositories, testing for continuous system integration in cloud environments, cloud-based architecture microservices and mobile computing and IoT systems, as well as the application of artificial intelligence techniques to software engineering.

Student roles:
* Conduct a literature review on software decomposition, static and dynamic analysis, data flow analysis, Agentic AI, and LLM-based software engineering techniques.

* Design and develop methods that combine static, dynamic, and data flow analyses with AI-driven reasoning to support monolith-to-microservices decomposition.

* Investigate the use of Agentic AI and LLMs for architectural discovery, dependency analysis, and microservice boundary identification.

* Implement and extend a prototype framework capable of analyzing real-world monolithic applications developed in multiple programming languages.

* Evaluate the proposed framework on diverse software systems and compare its effectiveness against existing decomposition approaches using appropriate benchmarks and metrics.

* Collaborate with researchers and students, document findings, and regularly present progress, experimental results, and recommendations.

Skills required:
- Strong understanding of software engineering principles, particularly monolithic and microservices architectures.
- Proficiency in multiple programming languages (e.g., Java, Python, C#, JavaScript).
- Proficiency with Graph theory and Compiler design.
- Familiarity with microservices communication protocols (REST, gRPC)
- Basic understanding of microservices deployment tools (docker, Kubernetes, etc)

Research area: My research focuses on developing secure and privacy-preserving AI systems with applications in cybersecurity, healthcare, smart and connected environments, and trustworthy intelligent systems. I have worked on behavioral biometric authentication, federated learning frameworks, adversarial attack and defense mechanisms, and the secure deployment of LLMs and Retrieval-Augmented Generation (RAG) systems. I have published in journals such as IEEE Access and MDPI, and in conferences including FPS, IFIP SEC, SECRYPT, IEEE BigData, and IEEE COMPSAC. I also have experience supervising students under the Mitacs GRI program, supporting their research training and project development.

Student roles:
The student will play a critical role in supporting the objectives of this research project on Agentic Federated Learning for Adaptive IoT Intrusion Detection. Their work will begin with an in-depth literature review on federated learning (FL), intrusion detection systems (IDS), IoT cybersecurity, adversarial machine learning, and agentic AI systems, particularly focusing on privacy-preserving and adaptive security mechanisms for distributed IoT environments. The student will assist in designing and implementing experiments to evaluate how federated and agent-based AI systems detect malicious activities across heterogeneous IoT devices without sharing raw data.

A key responsibility will involve developing scripts, simulation environments, and data processing pipelines for distributed intrusion detection tasks using benchmark cybersecurity datasets. The student will contribute to building autonomous agents responsible for tasks such as client selection, threat analysis, communication optimization, adaptive aggregation, anomaly detection, and defense against adversarial or poisoning attacks. They will also evaluate system performance using metrics such as detection accuracy, precision, recall, F1-score, communication efficiency, convergence stability, robustness, and privacy preservation.

The student will conduct comparative studies to assess how different federated learning strategies, agent collaboration mechanisms, and IoT network conditions affect intrusion detection performance and system scalability. Where appropriate, the student may explore lightweight defense mechanisms, communication-efficient aggregation methods, and adaptive response strategies for mitigating evolving cyber threats such as DDoS attacks, botnets, spoofing, and malicious client behaviors.

Additional duties will include maintaining organized and reproducible codebases, preparing visualizations and summaries of experimental findings, and assisting in drafting research reports and manuscripts for publication. The student will participate in regular team meetings to discuss progress, experimental results, and future research directions. This role provides hands-on experience in federated learning, IoT cybersecurity, agentic AI systems, and trustworthy distributed AI, contributing toward the development of adaptive and privacy-preserving intrusion detection frameworks for real-world IoT deployments.

Skills required:
The student should have strong Python programming skills and knowledge of large language models (LLMs), agentic AI systems, federated learning, and machine learning. Experience with PyTorch or TensorFlow, federated learning frameworks, and LLMs such as GPT, BERT, or LLaMA is required. Familiarity with agentic programming, autonomous AI agents, prompt engineering, and multi-agent systems is highly desirable. Knowledge of IoT-based intrusion detection, network security, and traffic analysis will be considered an asset. A strong mathematical background, particularly in linear algebra, probability, and statistics, is also required to support research in adaptive and privacy-preserving AI-driven cybersecurity systems.

Research area: My research focuses on developing secure and privacy-preserving AI systems with applications in cybersecurity, healthcare, smart and connected environments, and trustworthy intelligent systems. I have worked on behavioral biometric authentication, federated learning frameworks, adversarial attack and defense mechanisms, and the secure deployment of LLMs and Retrieval-Augmented Generation (RAG) systems. I have published in journals such as IEEE Access and MDPI, and in conferences including FPS, IFIP SEC, SECRYPT, IEEE BigData, and IEEE COMPSAC. I also have experience supervising students under the Mitacs GRI program, supporting their research training and project development.

Student roles:
The student will play a central role in advancing the research on Agentic Multimodal Fake News Detection. The work will begin with a comprehensive literature review covering fake news detection, multimodal learning, large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI systems, with particular emphasis on misinformation in textual, visual, audio, video, and social media contexts.

The student will assist in designing and implementing experimental pipelines to evaluate how multimodal AI systems detect manipulated, misleading, or AI-generated content. This includes developing data preprocessing workflows and integrating multimodal architectures that combine LLMs, vision-language models, and autonomous agent frameworks.

A major responsibility will be building and coordinating specialized AI agents for tasks such as text analysis, image and video verification, audio authenticity assessment, metadata inspection, social context modeling, and evidence retrieval using external knowledge sources. The student will also contribute to fact-checking and reasoning modules enabled by LLMs and RAG-based systems.

The student will conduct systematic experiments to compare different multimodal fusion strategies, prompting techniques, and multi-agent collaboration mechanisms. Performance evaluation will include metrics such as accuracy, precision, recall, F1-score, robustness, and explainability, particularly across benchmark misinformation datasets.

In addition, the student will explore adversarial misinformation scenarios and lightweight verification strategies, including source credibility analysis and external evidence grounding. They will maintain clean, reproducible codebases, generate visualizations of results, and contribute to research papers and technical reports.

The role also involves active participation in regular research meetings, presenting progress updates, and contributing to future research directions. This position provides hands-on experience in multimodal AI, agentic systems, and trustworthy AI for combating modern misinformation challenges.

Skills required:
The student should have strong Python programming skills and a solid foundation in deep learning, large language models (LLMs), and multimodal machine learning. Experience with PyTorch or TensorFlow and foundation LLM models is required. Familiarity with agentic AI systems, multi-agent collaboration, retrieval-augmented generation (RAG), and prompt engineering is highly desirable. The candidate should be comfortable working with multimodal data including text, images, audio, video, social context, and metadata. Prior knowledge of NLP, computer vision, social media analysis, and misinformation detection is an asset. A strong background in linear algebra, probability, and statistics is also required for modeling and multimodal reasoning.

Research area: My research lies at the intersection of distributed systems and cybersecurity, with a focus on advancing security and trust in decentralized environments. I study how decentralized paradigms enable secure, reliable, and scalable coordination in multi-party systems — particularly for collaborative and decentralized machine learning (e.g., federated and split learning) — addressing challenges such as robustness, privacy, and verifiability. I also explore the use of agentic AI systems and protocols such as MCP for automated vulnerability detection and threat analysis. In parallel, I investigate blockchain infrastructures, including Layer 2 solutions, cross-chain interoperability, and decentralized identity, as foundational building blocks for secure,

Student roles:
Intern 1 — Blockchain Environment & Attack Simulation:
Develop a simulated cross-chain messaging environment capturing key elements of bridge systems
Implement a set of representative attack scenarios (e.g., replay attacks, forged messages, simple inconsistency cases)
Generate and document a structured dataset of normal and malicious messages for evaluation
Collaborate with Intern 2 on integration and testing

Intern 2 — Multi-Agent Security Framework:
Design and implement a simplified multi-agent analysis pipeline with a limited number of agents (e.g., validation, consistency check, anomaly detection)
Integrate LLM-based or rule-assisted components where appropriate
Define and implement a basic evaluation framework, including metrics such as detection accuracy, false positives, and runtime overhead
Collaborate with Intern 1 on integration and testing

Skills required:
Candidates should have a strong foundation in computer science, particularly in distributed systems, computer networks, and cybersecurity, along with a working understanding of blockchain fundamentals, cross-chain protocols, and common security vulnerabilities. Proficiency in one or more programming languages such as Python, Rust, Go, or Solidity is expected. Exposure to AI concepts, especially agentic systems or LLM-based tools, and familiarity with blockchain development or security analysis tools are considered assets. Candidates should also be comfortable working in a research-oriented environment, with the ability to design experiments and analyze results.

Research area: My specialized research area is artificial intelligence, machine learning, and computer vision, with a particular focus on developing robust and interpretable methods for image and video understanding. My research investigates deep learning, graph neural networks, vision-language models, and multimodal learning for applications including semantic segmentation, anomaly detection, activity recognition, multimedia forensics and medical image analysis. A central theme of my work is the design of learning algorithms capable of operating under limited supervision, domain shifts, and uncertain real-world conditions.

Student roles:
The student will actively participate in all stages of the research project, including literature review, development of agentic AI systems, experimental evaluation, and dissemination of research outcomes. The primary responsibilities will involve implementing and testing large language models, vision-language models, and computer vision algorithms for disaster scene analysis, as well as preparing multimodal datasets composed of images, videos, geospatial information, and textual reports. The student will contribute to the design and evaluation of collaborative AI agents capable of performing tasks such as damage assessment, object detection, semantic segmentation, information retrieval, situational reasoning, and question answering in disaster environments. He/she will conduct experiments, analyze results, compare different agent architectures and reasoning strategies, and document findings through technical reports, presentations, and scientific publications. The student will work closely with the research supervisor and other team members, participate in regular research meetings, and contribute to the development of innovative AI solutions that support emergency response and disaster management decision-making.

Skills required:
The ideal student should have a strong background in computer science, artificial intelligence, or a related discipline. Prior knowledge of machine learning is highly desirable, particularly convolutional neural networks, transformers, and representation learning. Experience with Python and deep learning frameworks such as PyTorch is recommended. Familiarity with computer vision, image processing, or video analysis techniques would be an asset. The student should possess good analytical and problem-solving skills, an interest in research and experimentation, and the ability to read and understand scientific literature.

Research area: This project is in AI security, cybersecurity, and smart agriculture. The research focuses on how large language model-based assistants may be used in digital agriculture, including farmer advisory systems, agri-food decision support, and AI-enabled farm operations. The project examines safety risks such as prompt injection, misleading agricultural advice, data leakage, insecure tool use, and unreliable AI-generated recommendations. The work contributes to cybersecure and reliable AgFoodTech by developing evaluation methods and safeguards for responsible AI deployment in agriculture.

Student roles:
The intern will work under supervision to design, implement, and evaluate a research study on the security and safety of LLM-based assistants for smart agriculture. The student’s first task will be to review selected literature on LLM safety, prompt injection, AI security, and digital agriculture. Based on this review, the intern will help define realistic AgFoodTech use cases where AI assistants may be deployed.

The intern will then create a set of test scenarios and prompts that simulate both normal and adversarial interactions with an agricultural AI assistant. These scenarios may include misleading user instructions, unsafe farm-operation advice, confidential data requests, manipulation of retrieved information, or attempts to override system rules. The student will run experiments using selected LLM tools or APIs, record the model responses, and analyze failure patterns.

The intern will contribute to coding scripts for prompt testing, organizing experimental results, generating tables or visual summaries, and preparing a final research report. Depending on progress, the intern may also help design a simple prototype interface or dashboard for demonstrating the testing workflow. The student will meet regularly with the supervisor and research team, present weekly progress, and contribute to a final presentation suitable for the SECURE-AGRO research training environment.

Skills required:
The ideal student will have a background in Computer Science, Software Engineering, Computer Engineering, AI/ML, or Cybersecurity. Basic programming experience in Python is expected. Familiarity with large language models, APIs, prompt engineering, machine learning, data analysis, or security testing would be an asset but is not mandatory. The student should be comfortable reading research papers, designing experiments, documenting results, and working independently on a structured research problem. Interest in agriculture, food systems, or responsible AI is highly desirable.

Research area: Dr. Abdulrazak research interests include Ambient-Intelligence (AmI), IoT, Smart-Homes, Smart-Cities and Digital health. He works on various R&D projects at the AMI laboratory. The projects range from Assistive Home Environment for the aging people, Electronic Organizer, Pervasive Reminder System, Mobility Assistance, Smart cities for active aging, etc. These technologies aim at being used either inside or outside (city, home, retirement communities, community clinics, on the street, etc.)

Student roles:
The selected intern will operate as a research engineer, bridging the gap between theoretical optimization math and production-grade decentralized software architecture. The core mandate centers on translating sophisticated FL formulations into modular, benchmarking-compliant code components.
Key responsibilities and expected deliverables include:
• Algorithmic Analysis: Conducting a deep theoretical analysis of selected Federated Learning optimization methods to deconstruct their convergence patterns and communication requirements.
• Modular Codebase Implementation: Engineering a clean, highly modular, and production-ready software implementation of the evaluated FL strategies, fully integrated into the AMI-Lab 's existing framework patterns.
• Empirical Benchmarking & Quality-of-Service (QoS): Executing exhaustive experimental evaluations using standard reference datasets under highly variable data partitioning conditions (e.g., Non-IID data setups) to rigorously assess system QoS, communication efficiency, and predictive accuracy.
• Scientific Reporting: Documenting architectural trade-offs, experimental baselines, and data analytics outcomes in a structured, paper-style technical overview.
Through this internship, the student will gain highly marketable expertise in distributed systems engineering, edge AI optimization, and rigorous scientific benchmarking workflows. These competencies are exceptionally sought after in premier graduate research programs and cutting-edge industrial AI laboratories, providing a direct pathway to impactful, publishable contributions.

Skills required:
Core Qualifications:
• Research Motivation: Superior self-motivation to navigate rigorous algorithmic design and experimental R&D challenges.
• Core Development: High proficiency in modern programming methodologies, clean code practices, and software architecture.
Technical Competencies & Assets:
• Machine Learning: Strong foundational knowledge of core machine learning concepts, optimization techniques, or deep learning frameworks.
• Systems & Networking: Recommended familiarity operating within Linux environments and developing Web Services (APIs, distributed messaging).
• Hardware Context: Basic curiosity or background in embedded systems topologies or decentralized architecture constraints.

Research area: The laboratory area of expertise covers design and evaluation of interactive and intelligent assistive systems using multi-modality including haptic as communication channel and artificial intelligence. These systems are usually portable, but also concern design of semi-autonomous robotic mechanisms such as cable driven parallel robots. The main objective is to design, implement and evaluate technologies for people with disability or in loss of autonomy.

Student roles:
The candidate must develop in the RoboDK commercial software the disassembly steps which include:
1- definition of the steps in an AND/OR, BT or HNN graph for a particular product
2- optimization of the sharing of tasks between the human and the robot
3- guidance of the robot by numeric vision

Skills required:
The student must master techniques in industrial automation. Preferably, the ideal candidate has knowledge of rotation matrices, computer vision on OpenCV, robotic control and optimisation technics, VLM and LLM. Finally, knowledge of robotics, in particular serial manipulators is desirable. An additional asset would be to have knowledge of RoboDK and/or Gazebo, NVIDIA Isaac Lab.

Research area: My research is focused on robotics and artificial intelligence for engineering applications. In the past few years, through the support of the NSERC Industrial Research Chair (IRC) program, my team developed several human motion capture/training tools. For example, a camera-based system for tracking human head motions was developed using an AI tool, called Conventional Neural Network (CNN), that took the features of human images, such as nose, two eyes and two month corners, to compute the head motions while its turning sideways or up/down based on the changes in these feature positions, see https://youtu.be/Iw4okcAkVgA.

Student roles:
1. Platform preparation
The research assistant (RA) will learn how to program in the selected simulation platform Issac SIM, followed by converting the Visual Studio code of controlling a total of twenty-nice actuators of the robot into a code for Issac SIM and reprogram it to create a basic simulation platform for the robot joint motion control simulation. The RA will be tasked to identify the robot motion limits and payload capability based on the joint motion limits in order to establish safety guidelines for operation and implementation.
2. AI control simulation
In the second task, the research assistant (RA) will build an AI-based motion control simulation. The conventional robot control is based on inverse kinematics, i.e. a task is defined by a path in the global coordinate frame, and then the joint angles and speeds for the actuators to be controlled are solved numerically. For humanoid robots, due to the motion complexity and a high number of actuators (29 vs conventional 6), the common practice is to capture human motions in terms of human joint angles and input them into a humanoid robot controller to replicate the human motion.
3. Control testing
At the end, the research assistant (RA) will work my team to validate the simulation results by implementing the simulated robot motions on the actual humanoid robot in the laboratory. The validation will start with a simple task, for example, controlling the robot walking to a workstation and then grasping an object using one hand. Our humanoid robot is equipped with a three-figurer gripper for each of two hands. The second test will be using two hands to lift an object. The combined motion can be applied to an industrial production line for assembly or inspection.

Skills required:
Students in computer engineering or mechatronics engineering with the following skills/knowledge
- Basic knowledge about robots, better knowing humanoid robots
- Basic knowledge about actuator control, better knowing robot joint control
- Basic knowledge about AI simulation, database Github
- Fluent programming skill of Python
- Technical writing and presentation skill

Research area: An Inverse Design Platform for Autonomous Quantum Technologies is an emerging interdisciplinary research area that combines quantum computing, artificial intelligence, optimization, and automated experimentation to accelerate the development of next-generation quantum systems. Instead of relying on manual trial-and-error design, inverse design begins with desired performance objectives and automatically discovers optimal quantum hardware, software, control protocols, and system configurations. By integrating machine learning, digital twins, quantum simulation, and closed-loop experimentation, autonomous quantum platforms can continuously generate designs, execute experiments, analyze results, and refine solutions with minimal human intervention. This approach aims to significantly reduce development time of advanced quantum technologies.

Student roles:
The student will play an active role in the research and development of the Inverse Design Platform for Autonomous Quantum Technologies. Working closely with the principal investigator and research team, the student will contribute to the design, implementation, testing, and evaluation of software tools that enable automated optimization and autonomous operation of quantum systems.
The student’s primary responsibility will be to assist in the development of computational frameworks that combine artificial intelligence, machine learning, optimization algorithms, and quantum simulation techniques. This includes implementing software modules for data collection, model training, performance analysis, and design-space exploration. The student will also contribute to the development of digital twin models that represent quantum hardware and software environments, allowing candidate designs to be evaluated in simulation before deployment.
As part of the research process, the student will conduct literature reviews to identify state-of-the-art methods in inverse design, quantum computing, autonomous systems, and machine learning. The student will analyze existing tools and frameworks, compare alternative approaches, and help define technical requirements for the platform. Experimental tasks may include generating datasets, executing simulation campaigns, evaluating optimization strategies, and analyzing the performance of proposed solutions using quantitative metrics.

The student will participate in regular research meetings, present progress updates, document research findings, and contribute to technical reports, publications, and project deliverables. They will gain experience working in an interdisciplinary environment that integrates computer science, artificial intelligence, quantum technologies, and advanced software engineering.
Throughout the project, the student will receive training in quantum computing fundamentals, machine learning techniques, scientific software development, and research methodologies. They will develop practical skills in programming, data analytics, simulation, algorithm design, and technical communication. By participating in all phases of the project, the student will gain valuable hands-on experience in one of the fastest-growing areas of advanced computing and quantum technology research.

Skills required:
The student should have a strong background in computer science, software engineering, electrical engineering, physics, or a related field, with knowledge of programming and algorithm development. Experience with Python, machine learning frameworks, data analysis, and scientific computing is highly desirable. Familiarity with quantum computing concepts, optimization methods, simulation tools, or artificial intelligence techniques would be beneficial but is not required. The student should possess strong analytical and problem-solving skills, an interest in emerging quantum technologies, and the ability to work independently as well as collaboratively within a multidisciplinary research team. Strong written and verbal communication skills are also essential.

Research area: Generative AI, mental health

Student roles:
1) Analyze the literature relevant to the proposed project
2) Help design the LLM-based prototype
3) Help implement and deploy the LLM-based prototype
4) Help carry out experiments on the LLM-based prototype to assess its performance
5) Write a research paper to report the outcomes of the project.

Skills required:
1) Be fluent in English
2) Have excellent writing and oral skills
3) Have a basic knowledge of Generative AI technologies (e.g., Gemini, Llama, GPT)
4) Be very creative
5) Be team-oriented
6) Have excellent analytical skills
7) Master the Python programming language
8) Be enrolled in a computer science program.

Research area: Shooting sports are very popular in Canada, and represent a multi-billion dollar Canadian industry. Many participants choose to create (“load”) their own ammunition, following published “recipes” which prescribe particular components and amounts, and report the barrel pressure and projectile velocity produced. However,Covid’s supply chain disruptions and other global events have altered the selection of components available, and recipes that accommodate the currently-available components do not always exist. This research program creates open-source machine-learning, hardware, and software tools to make reloading more practical, affordable, and reliable.

Student roles:
A team of local electrical engineering students will design the hardware device from September 2026 to April 2027. You will create the software to complete the system. This will be a full-stack development project including databasing, signal processing, data visualization, and user interface design. There is potential to have beta testers providing actual user feedback throughout the process. You will be a named contributor to the open-source project.

Skills required:
Software development
Software development life cycle
Technical documentation
Database design
User interface design, user experience
Version control

Research area: Digital forensics focuses on the process of identifying, preserving, analyzing, documenting, and presenting evidence from digital sources. One of such digital sources includes digital documents which are often used for many business and personal communications. Our work in the area of questioned digital document examination focuses mainly on many of the most commonly used document types, including Portable Document Formats (PDF) and MS Office documents with the aim of providing techniques and tools to support the identification of document tampering, document origin, document age etc., particularly when such documents are questioned during forensic investigations.

Student roles:
The student will be required to do background research to understand the field of digital forensics, computer security, and have experience in data structures and algorithms and their application in computing. The student may be required to implement certain algorithms in an applicable programming language (e.g., Java, Python, C, or C++). The student may be required to learn to use some open-source digital forensics tools and apply them in the analysis of digital evidence sample data. The student will be involved in writing up results and preparing them for publication. The student should be able to work well with others and be comfortable with working in a research area where the existing literature may be limited.

Skills required:
We seek students with prior knowledge of computer security and privacy and an interest in digital forensics. The student must have some mathematics background, demonstrate excellence in courses such as Computer security, Data structures and algorithms, Operating systems, Computer architecture, and system software, and be capable of working with or learning to use the Linux operating system as necessary. Prior knowledge of tree data structure will be particularly useful. The student must be able to work with minimal direct supervision and be willing to ask questions as necessary. Strong communication skills (written and oral) and some experience working on technical

Research area: My research focuses on translational bioinformatics and artificial intelligence for precision oncology, with an emphasis on developing data-driven biomarkers to predict cancer outcomes and treatment response. Trained in applied mathematics and computational biology, I specialize in analyzing high-dimensional, multi-modal biomedical data, including clinical, imaging (radiomics and pathomics), and molecular datasets. I develop and apply machine learning and explainable AI methods to uncover the molecular and phenotypic mechanisms underlying cancer progression and variability in treatment response. My work integrates heterogeneous data sources to identify clinically actionable biomarkers and build predictive models that support personalized treatment strategies.

Student roles:
The student will contribute to all stages of the project, from data preparation to model development and evaluation. The primary responsibility will be to acquire and preprocess whole slide imaging (WSI) datasets, including tasks such as tissue region selection, patch extraction, normalization, and data organization. The student will design and implement deep learning models using PyTorch, including conventional architectures (e.g., CNNs) and foundation models (e.g., vision transformers). This includes training, fine-tuning, and optimizing models for tasks such as tumor classification, grading, or subtype prediction. The student will also perform model evaluation and comparison, analyzing performance, robustness, and generalizability across approaches. Additionally, they will contribute to the interpretation of results, including identifying meaningful image-based features or potential biomarkers. Finally, the student will be expected to document the methodology, summarize findings, and present results to the research team.

Skills required:
The student should have strong programming skills in Python, with experience using deep learning frameworks such as PyTorch. A solid understanding of machine learning and deep learning concepts, including model training, validation, and evaluation, is required. The candidate should have prior exposure to medical image analysis. Familiarity with convolutional neural networks (CNNs) and/or vision transformers is beneficial.

Research area: Our lab is interested in the biology and treatment of acute myeloid leukemia in children and we use a range of high-throughput genomics approaches to better understand the therapeutic vulnerabilities. This involves large-scale drug screening, genome-wide CRISPR screens, as well as other genomics approaches. We also collaborate with a wide range of labs across the country, and develop web-based tools to allow other researchers to explore the data interactively.

Student roles:
The specific role for the Globalink student for this project will be focused primarily on devising and implementing algorithms for visualizing large sets of biological data. Students will also be encouraged to research new computational methods representing sequence data and allowing end users to interact with their data. The student would also be responsible for documenting computational tools and responding to feedback from members of the research team.

The student would have the opportunity to work within a supportive team composed of biologists, computer scientists and bioinformaticians. Within the global research project the student would also have the opportunity to choose specific research aspects to pursue which suit their interests and, under supervision, to develop novel and attainable research goals.

Skills required:
The ideal student will be in computer science, bioinformatics, software engineering, or a related field, with experience in Python and/or JavaScript. Familiarity with data analysis libraries (e.g., pandas, NumPy) and basic statistical concepts is expected. Exposure to web-based development frameworks (such as React, D3.js, or similar) and database systems (SQL or NoSQL) is highly desirable. The student should be comfortable working with large, complex datasets and have an interest in biomedical data, particularly cancer genomics. Strong problem-solving skills, attention to detail, and the ability to collaborate in an interdisciplinary research environment are essential.

Research area: My research focuses on advancing the reliability, quality, and trustworthiness of software-intensive systems through the application of artificial intelligence and data-driven techniques. I am particularly interested in software engineering, machine learning for software systems, distributed systems reliability, requirements engineering, and trustworthy AI. My work investigates how machine learning and large language models can be leveraged to improve software development processes, including early detection of requirements defects, software quality assurance, anomaly detection, and automated verification. I am also interested in understanding and mitigating biases in AI systems, developing explainable and reliable AI-assisted software engineering tools.

Student roles:
The student will participate in the following activities:

* Developing software algorithms that identify programming language bias in LLM.

* Document, analyze, discuss, and represent the obtained results and the developed systems.

* Perform weekly meetings with the supervisor.

* Build systems (e.g., machine learning models) that analyze the source code and recommend the suitable implementation to the requested features.

* Write a final report that summarizes the background, approach, the obtained results, and the future directions.

* Make presentations, attend a conference.

Skills required:
Basic knowledge in machine learning skills
Strong programming skills (preferred python and R)
Background in Computer Science
Familiarity with LLMs
Strong communication skill (English)

Research area: My research interest focuses on Software Engineering (SE) support for complex AI-based software systems (quality assurance for AI). My most recent works cover software testing, analysis, and improving for AI models and systems. Additionally, I am broadly interested in the intersection of Software Engineering, AI, and Human-Computer Interaction (HCI). I design and develop interactive interfaces to help developers interpret and debug AI-based software.

Student roles:
The student will participate in research activities under the supervision and guidance of the faculty member. Responsibilities include conducting literature reviews, implementing and evaluating research prototypes, analyzing experimental results, and assisting with the preparation of research papers. The student is also expected to actively communicate progress, collaborate with other team members, and contribute to regular meetings and discussions.

Skills required:
1. Strong programming skills and solid foundations in computer science and/or software engineering.
2. Willingness and ability to learn new cutting-edge knowledge.
3. Good communication and collaboration skills.
4. Good English proficiency, particularly in reading technical papers.
5. Previous experience with software engineering agents (e.g., SWE-Agent) is preferred.

Research area: Machine Learning Cloud computing Big data Artificial Intelligence Computer networks Smart technologies

Student roles:
Reading literature review
Setting up the environment and practicing examples
Designing the system
Implementing the system
Carrying out experiments and collecting data
Preparing technical reports and packaging the code
Attending weekly meetings with supervisors and the team

Skills required:
Programming capability
Knowledge of database and data processing
Knowledge of machine learning

Research area: My research area and expertise focuses on unsteady turbulent flow dynamics and modeling. This includes external and internal flows with applications in energy harvesting, energy storage, energy transportation, low emissions aerial, ground and underwater vehicles. Particularly, my team has developed a global reputation for research in the areas of unsteady turbulent wake dynamics and turbulent pipeflow dynamics. Moreover, our efforts have led to and continue to result in the development of new technologies and products that are revolutionary in design to lower GHG emissions and enhance our technological capabilities.

Student roles:
The student will complete pre- and post-processing of pipeflow-like simulations related to the blood flow inside the aorta, and modify existing ML-based code to account for the strong FSI effects and pulsation in biomedical applications. They will work closely with another PhD and MSc student that are already working on the project and provide them support and new insights.

Skills required:
The student needs to have a good understanding of fluid mechanics, structural mechanics and fluid-structure-interactions with familiarity in SolidWorks, python and OpenFOAM/ANSYS. The right candidate will need to complete literature review, post-process results and complete simulations on pipeflow-like geometries. Knowledge and familiarity with OpenFOAM and Tecplot are considered a plus.

Research area: Dr. Travis Esau specializes in precision and digital agriculture, focusing on the design and development of mechanized and autonomous systems to enhance farm productivity, sustainability, and profitability. His research integrates machine vision, artificial intelligence, and geospatial technologies to enable data-driven decision-making in crop production systems such as wild blueberry and potato. He advances innovations in automated harvesting, precision spraying, and field monitoring, while also addressing farm traceability and cost-of-production analysis. Through applied, field-validated research, his work supports the transition toward Agriculture 4.0, improving efficiency, reducing environmental impact, and strengthening the resilience and competitiveness of modern agricultural systems.

Student roles:
The project will produce a functional mobile application capable of real-time agricultural analysis using embedded machine learning models. Deliverables will include integrated and optimized models for detection and prediction tasks, a structured system for linking outputs to agronomic fact sheets, and a complete application pipeline supporting real-time inference. Additional outputs will include documented codebases for both model deployment and application logic, performance evaluations assessing inference speed and accuracy on mobile hardware, and curated datasets or model configurations used within the application. Prototype demonstrations of the application in field conditions will be developed, along with internal technical documentation describing system architecture, implementation details, and performance characteristics. Contributions to research outputs or technical presentations may also be produced where applicable.

Skills required:
The student should have a strong background in machine learning, mobile application development, and software integration. Experience deploying and optimizing models for mobile environments (e.g., quantization, model conversion) is essential. The student should be comfortable integrating multiple models into real-time inference pipelines and working with image-based data. Familiarity with backend development, data flow management, and user interface design is important. Knowledge of predictive modeling (e.g., time-to-harvest, growing degree days) is an asset. The student should be capable of testing and validating applications under field conditions, with strong problem-solving, organizational, and communication skills, and the ability to collaborate and provide regular

Research area: My specialized research area focuses on data privacy and security, with an emphasis on digital privacy, usable privacy, and searchable encryption methodologies. I study how user behavioral data, such as interaction patterns and system usage traces, can unintentionally lead to privacy violations. My research aims to advance usable privacy by designing privacy‑preserving systems that are understandable, practical, and minimally disruptive to users, ensuring that strong privacy protections can be effectively adopted in real‑world computing environments without sacrificing usability or functionality.

Student roles:
The undergraduate student will support the research project by actively contributing to both analytical and implementation tasks under close supervision. The student will assist with conducting a literature review on app tracking technologies, privacy violations, and existing mitigation approaches, helping to summarize and organize relevant research. The student will help with conducting the research, including examining how applications integrate trackers, analyzing data collection and sharing behaviors, and identifying potential privacy risks. In addition, the student will contribute to the design of a research framework for evaluating app trackers from a privacy and usability perspective. The student will also assist with the implementation of a prototype aimed at improving transparency or mitigating tracker‑related privacy violations, as well as testing and documenting the results.

Skills required:
Ability to assist with literature review and academic research
Interest in data privacy, security, and digital privacy issues
Familiarity with programming languages (familiarity with mobile programming)
Basic understanding of mobile or web application development concepts (preferred but not required)
Analytical thinking and willingness to learn privacy analysis methodologies
Motivation to engage in interdisciplinary research combining technology, privacy, and usability

Research area: Centre for Pavement and Transportation Technology (CPATT) at the Faculty of Engineering of the University of Waterloo covers a broad range of research projects spanning from developing innovative and sustainable materials from nano-scale to micro, macro and full scale pilots as well as application of technology to transportation infrastructure. Application of AI and Machine Learning as well as image processing techniques has become the core of many research projects that we currently have at the CPATT.

Student roles:
Conduct a scoping literature review in a timely manner.
Design and assist with conducting experiments and collecting images and videos to be processed either realtime or asynchronously later.
Develop scripts to process the image frames
Develop a user interface for performing the DIC analysis
Attend weekly and monthly group meetings and helping with taking the minutes and organizing activities

Skills required:
The candidate should have a strong programming skill and be proficient in Python and Matlab at least.
Conducting a review of the literature and summarizing the state of the art and practice in the relevant area of the proposed research. Working efficiently is a key. Good time management and technical writing skills are must for a successful candidate.
Experience with Image Processing will be an asset.
The candidate will be working in a big multidisciplinary group and interpersonal skills and team work capabilities are necessary.

Research area: This research focuses on the application of artificial intelligence, particularly deep learning and reinforcement learning, for medical image analysis. Specifically, we are interested in the ultrasound-based assessment of thyroid nodules, with the goal of improving diagnostic accuracy and supporting clinical decision-making.

Student roles:
The student will contribute to the development and evaluation of artificial intelligence methods for thyroid nodule assessment using ultrasound imaging. Their role will include implementing and training deep learning models, exploring reinforcement learning approaches for analyzing ultrasound video data from low-cost point-of-care ultrasound (POCUS) devices, and comparing AI-based results with conventional clinical reporting systems such as TI-RADS. The student will also assist in data preprocessing, model evaluation, and result interpretation.

Skills required:
The ideal student should have a background in computer science, biomedical engineering, electrical engineering, or a related field.
Experience with machine learning, particularly deep learning, is highly desirable
Familiarity with Python programming and common ML frameworks such as PyTorch or TensorFlow is important
Knowledge of medical image analysis, especially ultrasound imaging
Strong analytical skills and the ability to work independently and collaboratively in a research environment

Research area: My research interests are at the intersection of social/personality psychology, educational psychology, and applied linguistics, especially where they converge on motivation, identity, intercultural communications, and intergroup relations. One line of my research focuses on beliefs and motivation that influence language development and intergroup/interpersonal processes. Current projects include examining social factors that shape mindsets and goal pursuits related to the success of language learning and interpersonal/intergroup communication. Another line of my research focuses on acculturation and socio-cultural adaptation, particularly their relations with discrimination, identity, and language development.

Student roles:
The intern's research project is part the bigger project (title "Applying AI to Understand Migrants’ Experiences in Language Learning and Cultural Adaptation"), which involves research and development of an educational app that aims to inform migrants' socio-cultural and linguistic adaptation, especially in relation to mental health. The student's overall objective is to investigate the effectiveness of the program by assisting with data collection and qualitative data analysis for both academic research and product development contexts. Therefore, students participating in this project should have experience in research design, data collection (e.g., interview), and quantitative data analysis. The students may participate in a couple of the following tasks:
1. Assist in collecting data from a project about an online mental health program, such as conducting interviews
2. Conduct qualitative and quantitative analyses.
3. Contribute to knowledge dissemination efforts, including but not limited to manuscript development and conference submissions.
4. Translate the research materials and report (e.g., white paper) for different audience.

Skills required:
Students participating in this project should have an interest in educational design, knowledge in AI research, and a strong statistical/analytic skills in psychology research. Experience in data collection, such as conducting interviews, is required. Additionally, proficiency in both qualitative and quantitative research methods is necessary.

Research area: Mes recherches portent sur le développement d'une nouvelle génération de modèles d'intelligence artificielle visant à mieux représenter les systèmes hydrologiques et à faciliter la gestion des ressources en eau. Plus précisément, je développe des modèles hybrides qui combine les connaissances physiques des systèmes hydrologiques avec des méthodes d'apprentissage automatique.

Student roles:
L'étudiant participera à l'élaboration et à la comparaison de modèles d'apprentissage profond destinés à la prévision des débits fluviaux. Au cours de la première partie du stage, il se familiarisera avec l'ensemble de données hydrologiques, l'objet de la recherche et les concepts fondamentaux de la modélisation des bassins versants et de la prévision des séries chronologiques.

Il préparera ensuite les données d'entrée pour les modèles d'apprentissage automatique, notamment les séries chronologiques relatives aux précipitations, à la température, à la neige et aux débits fluviaux. La principale tâche de recherche consistera à mettre en œuvre et à tester des modèles d'apprentissage profond tels que les réseaux LSTM et GRU. Ces modèles seront entraînés pour apprendre comment les conditions météorologiques influencent le débit des cours d'eau au fil du temps.

L'étudiant comparera les modèles à l'aide d'indicateurs de performance standard et produira des graphiques illustrant les débits observés et simulés. Si le temps le permet, l'étudiant pourra également explorer l'apprentissage par transfert en entraînant un modèle sur plusieurs bassins versants et en vérifiant si cela améliore les simulations pour certains bassins versants cibles.

L'étudiant rencontrera régulièrement son directeur de recherche pour discuter de l'avancement des travaux, résoudre les problèmes techniques et interpréter les résultats. Les livrables finaux comprendront du code Python documenté, des graphiques, un bref rapport technique et un résumé des principales conclusions. Les résultats pourraient servir de base à une future publication scientifique ou à un projet de recherche plus vaste sur l'IA appliquée à la gestion des ressources en eau.

Skills required:
L’étudiant ou l’étudiante devrait posséder des bases en programmation Python et démontrer un intérêt pour l’apprentissage automatique, la science des données ou la modélisation hydrologique. Une expérience avec des bibliothèques comme NumPy, pandas, scikit-learn, PyTorch ou TensorFlow constitue un atout, sans être obligatoire. La personne devra être à l’aise avec les données numériques et intéressée à apprendre les concepts de base en hydrologie. La curiosité, l’autonomie et les habiletés de communication sont importantes. Une expérience en analyse de séries temporelles ou en réseaux neuronaux serait un avantage.

Research area: I hold a Ph.D. in Computer Science and Engineering. My research focuses on AI, machine learning, and cybersecurity. During my Ph.D., I specialized in developing smart systems using big data analytics and AI. Over the past six years, my work has focused on designing novel information security and privacy protocols for surveillance systems. I have employed deep learning and AI techniques in combination with homomorphic encryption to establish privacy-preserving surveillance through video, audio, and speech. In these areas, I have published over 70 peer-reviewed articles in journals and conferences, along with 4 book chapters, with more than 5,000 citations.

Student roles:
The student will play a role in the design, implementation, and evaluation of a deep learning-based framework for physical art image authentication and verification. This includes:
• Conducting a thorough literature review of existing methods utilizing deep learning models for image authentication and forgery detection.
• Assisting in preprocessing a dataset of high-resolution physical art images, including both original and similar samples.
• Using tools such as TensorFlow or PyTorch, implementing and training deep neural networks (e.g., CNNs, Siamese networks, or transformer-based architectures) to identify fine-grained visual signature patterns for authenticity. The student will explore model architectures and tuning strategies to optimize accuracy and robustness against adversarial inputs.
• Integrating blockchain components, such as hashing verified image features and securely storing them on a distributed ledger for immutable verification. Basic knowledge of smart contracts or platforms like Ethereum or Hyperledger may be leveraged.
• Evaluating system performance based on metrics such as accuracy, precision, recall, and computational cost. The project may include building a lightweight proof-of-concept interface to demonstrate the solution.
• Preparing documentation and contributing to writing a report or publication summarizing the findings and potential real-world applications in galleries, museums, or online art markets.

Skills required:
• The student should be enrolled in a full-time undergraduate or combined undergraduate/master’s program in Computer Science or a related field.
• They must have completed courses in machine learning, computer vision, and programming (e.g., Python, C++, or Java) with strong academic performance.
• Familiarity with deep learning frameworks such as PyTorch or TensorFlow is essential.
• Experience with blockchain technologies or cryptographic principles is highly desirable.
• The student should possess good problem-solving abilities, strong communication skills, and academic writing proficiency.
• Knowledge of image processing techniques and an interest in art or digital forensics.

Research area: My research intersects Human-Computer Interaction, Artificial Intelligence (AI), and Persuasive Computing to design, develop, and evaluate AI-driven, user-adaptive, and highly personalized technologies for public and social good across diverse domains including health and wellness, education, security, and sustainable environment. These technologies deliver evidence-based, personalized, and adaptive interventions in real-time while operationalizing appropriate persuasive strategies to motivate and promote desirable behaviours in individuals and groups.

Student roles:
The student will work under supervision to develop and evaluate an AI-driven personalized digital health app. First, they will survey recent AI apps in digital health, focusing on a specific domain (e.g., mental health promotion or diabetes prevention), to identify relevant physiological and contextual data, data sources (wearable sensors and smartphone), and evidence-based intervention strategies. Collaborating with the supervisor, they will finalize the feature sets, evaluation metrics, and a data-collection protocol that ensures user privacy and compliance with best practices. Next, the student will implement data pipelines to collect raw sensor streams and user-entered logs. They will clean and normalize multivariate time-series data by handling missing values, synchronizing timestamps, and engineering initial feature sets. Throughout this phase, detailed documentation of data schemas, preprocessing steps, and user privacy measures is expected. Next, the student will experiment with several popular supervised and unsupervised machine learning (ML) algorithms, starting with a baseline model and iterating toward more sophisticated architectures. They will evaluate model performance and report metrics (e.g. accuracy, F1-score, etc.) and also choose the overall best performing ML model. Next, the student will design and develop a cross-platform mobile app that collects sensor data and user-entered logs, runs the selected ML model for inference (via a lightweight backend or API endpoint), and provide personalized interventions based on predicted outcomes. They will implement persuasive features - such as gamification (e.g., rewards in form of points, badges, streaks, etc.), reminders, goal setting, progress tracking, and social support - to keep users motivated and engaged. Finally, the student will conduct small-scale usability study (5-10 participants), gather quantitative and qualitative feedback, and refine the app based on the feedback.

Throughout the project, the student must maintain code repositories with proper version control and inline documentation, submit a concise weekly progress report, and produce a final report.

Skills required:
The student should have some experience in software development and machine learning (ML). Proficiency in programming languages such as Python is essential, since developing ML models, preprocessing sensor data, and integrating diverse data sources often rely on Python libraries like scikit-learn, TensorFlow, or PyTorch. Also, the student should be proficient in cross-platform mobile app development (using frameworks like Flutter), prototyping user interfaces (using tools like Figma), database design, and development of REST APIs. Given the sensitive nature of user data, the student should be comfortable working with privacy-preserving techniques such as data anonymization and encryption. Communication in English is required.

Research area: My research focuses on digital health, rehabilitation science, and artificial intelligence (AI), aiming to develop equitable, inclusive, and technology-enabled solutions for aging populations. I design and evaluate AI-driven interventions using wearable sensors and data platforms to enhance mobility, autonomy, and quality of life in older adults and individuals with disabilities. The work supports precision rehabilitation, telemonitoring, and self-management, especially in rural and underserved communities. Grounded in health equity and ethical AI, my research addresses the digital empowerment divide and contributes to evidence-based clinical innovation, policy development, and the advancement of patient-centered digital rehabilitation systems.

Student roles:
The student will play a central role in the technical development and refinement of an AI-powered mirror-mode rehabilitation system designed for older adults in long-term care (LTC) and community. Working closely with the research team, the student will be responsible for implementing and testing core system functionalities using real-time pose estimation (MoveNet), webcam-based video input, and visual feedback mechanisms.

Key Responsibilities:

System Development and Integration
• Implement real-time pose estimation using MoveNet or equivalent models
• Integrate movement classification, range of motion tracking, and repetition counting modules
• Develop mirror-mode visual feedback and compensatory movement alerts
• Ensure smooth system performance using standard webcams or tablet cameras

Interface and Usability Support
• Design and implement a user-friendly interface suitable for older adults
• Incorporate visual cues and cognitive prompts into selected exercises
• Collaborate with the team to adapt the interface based on feedback from LTC participants

Testing and Iteration
• Conduct initial system testing with volunteers
• Support usability testing in LTC settings by preparing the system, assisting during sessions, and troubleshooting as needed
• Refine system features based on participant and stakeholder feedback

Documentation and Collaboration
• Document code, system workflows, and updates clearly
• Participate in regular project meetings and contribute to interdisciplinary discussions
• Assist with the preparation of technical summaries or visuals for reports and presentations

Skills required:
The ideal student will be enrolled in a program in Computer Science, Software Engineering, Electrical or Biomedical Engineering, or a related technical field. This position is best suited for a student interested in applied AI and digital health technologies. The following skills and qualifications are required or strongly recommended:
Technical and Programming Skills
• Proficiency in Python
• Experience with machine learning libraries (e.g., TensorFlow, PyTorch, etc.)
• Familiarity with real-time video processing, pose estimation models (e.g., MoveNet, BlazePose), or media pipelines

System Integration and UI/UX
• Ability to integrate front-end and back-end components for a user-facing application

Research area: My research focuses on entrepreneurship, innovation, entrepreneurial ecosystems, and the strategic adoption of emerging technologies. I examine how entrepreneurs and SMEs leverage new technologies, including artificial intelligence, to enhance innovation, competitiveness, and business growth. My work explores the interaction between technological change, entrepreneurial decision-making, and organizational performance across different institutional and economic contexts. Through interdisciplinary research, I seek to understand how innovation capabilities are developed and how emerging technologies can support sustainable entrepreneurship and regional economic development.

Student roles:
The student will work closely with the faculty supervisor on a research project examining the role of artificial intelligence in entrepreneurship, innovation, and SME development. The position is designed to provide students with an opportunity to apply their technical or analytical expertise while gaining exposure to entrepreneurship and innovation research.

The student will contribute to literature reviews, environmental scanning, and the identification of emerging trends related to AI and entrepreneurial innovation. Depending on their background and interests, they may assist with collecting and organizing data, conducting case studies, reviewing AI applications used by SMEs, analyzing publicly available datasets, and supporting the development of research reports and presentations.

Students with technical expertise may contribute to data analytics, text analysis, dashboard development, or exploratory applications of AI tools. Students from business or interdisciplinary backgrounds may assist with case development, interviews, industry scanning, and analysis of entrepreneurial practices related to AI adoption.

A key expectation is that students will bring their disciplinary expertise to the project while remaining open to learning from complementary fields, particularly entrepreneurship, innovation, and technology management. Strong communication and writing skills are important, as students will contribute to research summaries, reports, presentations, and other knowledge mobilization activities.

Throughout the project, students will gain exposure to interdisciplinary research, digital innovation, entrepreneurship scholarship, and emerging technology trends. They will participate in research meetings and may have opportunities to contribute to conference presentations, case studies, and academic publications. The project is particularly well suited for students interested in future graduate studies in entrepreneurship, innovation, technology management, artificial intelligence, or related disciplines.

Skills required:
Applicants should have a strong academic background in Computer Science, Software Engineering, Data Science, Information Systems, Business Analytics, Management Information Systems, Entrepreneurship, or related disciplines. We welcome students who have demonstrated academic excellence in their field and are interested in exploring entrepreneurship, innovation, and emerging technologies. Familiarity with AI tools, data analysis, programming, or digital technologies is beneficial but not required. Strong communication, analytical thinking, and writing skills are essential. Curiosity, initiative, and an interest in interdisciplinary research are highly valued.

Research area: My specialized research area is in smart grids, smart energy systems, and AI-enabled power and energy applications. My work focuses on applying artificial intelligence, machine learning, and data-driven methods to problems such as load forecasting, renewable energy forecasting, intelligent monitoring, fault diagnosis, and energy management in modern power systems. I also work on the modeling, control, and optimization of power electronic converters, battery energy storage systems, renewable energy interfaces, and microgrids. Overall, my research aims to develop reliable, efficient, and intelligent solutions for the operation and modernization of future energy systems.

Student roles:
The student will assist with data preparation, model development, training, testing, and performance evaluation for AI-based smart grid and energy applications. Responsibilities will include organizing datasets, performing preprocessing and feature analysis, implementing machine learning models, comparing results using appropriate metrics, preparing plots and tables, and documenting findings in short technical summaries, a final report, and a presentation.

Skills required:
The student should be an upper-year undergraduate in electrical engineering, computer engineering, software engineering, data science, or a related field, with basic knowledge of programming, data analysis, and machine learning. Familiarity with Python, MATLAB, or common AI/ML tools would be beneficial. An interest in smart grids, energy systems, forecasting, monitoring, or data-driven analysis is desirable, along with good analytical, problem-solving, and technical communication skills.

Research area: artificial intelligence, machine/deep learning, data science, natural language processing

Student roles:
- contribution to research projects
- contribution to development of open-source prototypes, and to the evaluation of implemented systems
- discovering state-of-the-art literature for the research domains
- sharing ideas and acquired knowledge with team members
- contribution to the writing of scientific papers could be discussed

Skills required:
- good programming skills
- Linux OS
- autonomous
- able to work in a team
- interest for multidisciplinarity

Research area: Soft matter and complex fluids, polymer materials, non-Newtonian fluids, rheology, turbulence, computational fluid dynamics, molecular modeling, multiscale simulations, data-driven modeling, machine learning, artificial intelligence

Student roles:
The student will work with the supervisor and his research team and develop novel techniques in the application of machine learning, data-driven modeling, and artificial intelligence in various application areas within chemical, process, and/or materials engineering. We are interested in the application of these techniques in a broad range of areas, including polymers, rheology, fluid mechanics, and process engineering. As such, we are able to tailor the project to the specific background and interest of the student. The student will report to the supervisor on a weekly basis and will be asked to make at least one group meeting presentation and submit a written report by the end of the internship.

Skills required:
The candidate must have a background in engineering or science, and have completed college-level mathematics courses including calculus and linear algebra. The candidate is also expected to have learnt at least one programming language. Background and experience in at least one of the following fields are also desired: numerical/computational methods, machine learning, artificial intelligence, process modeling, optimization, materials modeling, and fluid mechanics. Command of the English language is required.

Research area: Industrial control and industrial automation (HMI/SCADA and PLC), artificial intelligence, machine learning, control theory, process monitoring, cybersecurity, fault diagnosis, alarm management

Student roles:
The student is expected to achieve the following:

- Operate industrial control and automation systems available on campus, including, industry-grade PLC and HMI, for data collection;
- Collect and pre-process operational data;
- Learn and modify PLC code and configuration, for experiment design;
- Learn and modify HMI screens, code, and configuration, for experiment design;
- Learn, develop, and test computational algorithms;
- Review and understand articles published in journals and conferences;
- Work with other group members as needed.

Skills required:
The student is required to have a strong background in the following:

- Mathematics (e.g., algebra, statistics, artificial intelligence, natural language processing, etc.);
- Logic and reasoning;
- Programming (Python or MATLAB);
- Written and oral communication; and
- Industrial control and automation (PLC and HMI programming and integration - preferred).

The student is required to also have a strong attitude toward:

- Independent learning and thinking;
- Innovation;
- Creativity; and
- Self-motivation.

Research area: Traffic and Transportation Engineering, Analysis, modeling and optimization of the transportation system (Mobility-As-A-Service, public transport, ride-sharing, and emerging transportation technologies), travel behavior and transport economics, data mining and decision analytics (statistical model, machine learning/deep learning), mechanism design and game theory.

Student roles:
Students will participate in the following research activities:

- Literature review in transportation infrastructure monitoring and defect detection research, technologies and applications (e.g., railway, road).
- Case study research for smart transportation infrastructure, services and facilities in Canada including data analysis, technical program and summaries
- Technical report and manuscript editing and formatting, assistant in conference papers and technical reports
- Support research group for machine learning algorithm validations and data labeling.
- Big data processing and correlation analyses

Skills required:
- Strong interests in traffic and transportation engineering, transportation facilities, machine learning algorithms and spatial-temporal data analyses.
- Relevant background in transportation engineering, electrical engineering, mechanical engineering, data science or computer science.
- Experience in data analysis and processing.
- Skilled in python.

Research area: My research focuses on improving software quality by managing Technical Debt (TD) in both traditional and scientific software. I conduct empirical studies with practitioners to understand real-world TD practices and leverage Artificial Intelligence and Empirical Software Engineering to automate TD management. Recent work includes using TD as a lens to identify security vulnerabilities, analyzing how developers induce and address TD through refactoring, and mining repositories to uncover TD patterns. I also study developer challenges on crowdsourcing platforms, aiming to inform intelligent, practical tools and solutions for sustainable software development.

Student roles:
-Week 1: Get set up and become familiar with the lab environment and the project; become familiar with the relevant libraries, tools, technologies, and core concepts; review relevant literature.
-Week 2-3: Define the data collection strategy and criteria for identifying coding-agent-authored pull requests involving TPLs based on related work.
-Week 4-5: Implement and test the repository mining pipeline; build an initial dataset.
-Week 6: Extract, clean, and validate the TPL data from the collected pull requests.
-Week 7: Perform vulnerability assessment of the extracted TPLs and analyze the results.
-Week 8-9: Refine the analysis by comparing vulnerability patterns across ecosystems and coding agents.
-Weeks 10–11: Write the project report.
-Week 12: Wrap up, including preparing and delivering the final presentation.

You will be required to write short daily and weekly reports on your progress, including the steps performed during the experiment. Your main responsibility is to follow the predefined steps and not to do research. You will be provided with the required techniques and will need to become familiar with the relevant libraries. Your input regarding the libraries and frameworks that you are familiar with will be considered. However, based on the results of earlier phases, we might need to tweak later phases of the project. You will integrate into a diverse and inclusive research group. You will be required to be punctual daily, work independently, and maintain a positive attitude. We will have weekly progress meetings, but will interact almost daily. You may get the opportunity to interact with our international collaborators and be a co-author of any publication resulting from this study.

Skills required:
The preferred background is in computer science, software engineering, or a closely related field. The student should be comfortable with software development (programming). The specific required skills are:

-Proficient in either Python or R and related environments such as Jupyter Notebooks, Quarto
-Familiar with coding agents and harnesses (OpenAI Codex, Claude Code, OpenCode, PI, etc)
-Familiar with core computer science and software engineering concepts
-Familiar with text mining and natural language processing
-Fluent in reading/writing/speaking English

Research area: Abdullah's research focuses on the development of robotic systems, with a particular emphasis on three core areas: therapeutic robots for the rehabilitation of individuals with impaired upper limbs, assistive robotic devices designed to enhance daily living, and exoskeleton robotic systems aimed at augmenting human mobility and strength. In addition to these focal areas, he has made significant contributions to the advancement of mechatronic system design. His expertise and leadership have been instrumental in securing and managing numerous research projects supported by both industry and funding organizations. His work has resulted in over 90 publications in international journals and conferences.

Student roles:
• Requirement Analysis: Determine user requirements, therapeutic objectives, and essential system functionalities for upper limb rehabilitation.
• System Design and Architecture: Develop the software structure, Unity workflow, and database schema to facilitate efficient system development.
• 3D Environment and User Interface Development: Collaborate with research team members to develop a virtual therapy environment, incorporating visual components and user interface elements within Unity.
• Motion Tracking Integration: Collaborate with research team members to connect and configure motion tracking devices for capturing and mirroring upper limb movements.
• Database Integration: Establish and connect a database to store user profiles, session data, and performance metrics.
• Evaluation and Iteration: Gather feedback from users or therapists and refine the system to enhance overall performance.
• Documentation and Deployment: Prepare comprehensive system documentation and ready the application for installation, demonstration, or clinical evaluation.

Skills required:
We are seeking a senior undergraduate or master's engineering student specializing in computer, systems, or computer science. The ideal candidate should have experience with the Unity Real-Time Development Platform, database design and administration, and working with relational databases such as MySQL or SQLite. Proficiency in writing efficient queries, ensuring data integrity, and integrating databases or APIs with Unity applications is essential for seamless data management.
Additionally, knowledge of cybersecurity best practices, data privacy, debugging techniques, and version control systems such as Git is highly advantageous for secure and collaborative development.

Research area: Prof. Pascal E. Fortin has been affiliated with the Computer Science and Mathematics Department of Université du Québec à Chicoutimi since 2022. He holds a PhD from McGill University. His main research expertise lies in human-computer interactions, with a specific focus on multisensory and physiological interaction techniques. Prof. Fortin’s current projects tackle the mechanisms of cybersickness in extended reality (XR) environments to design more comfortable, immersive systems. His work not only pushes the boundaries of interactive technology but also fosters interdisciplinary collaboration through strategic partnerships with local industries and governmental entities, driving innovation and producing tangible benefits.

Student roles:
The intern will contribute to the literature review on auditory vection, cybersickness, and multisensory perception. They will help design audio-visual stimuli, develop a VR prototype, prepare the experimental protocol, conduct pilot data collection, analyze the results, and contribute to the writing of a scientific report or short research paper.

Skills required:
Background in virtual reality, human-computer interaction, multisensory perception, experimental psychology, or a related field. Skills in Unity, C#, audio processing, spatial audio, or data analysis would be valuable. The candidate should be able to build experimental prototypes, analyze quantitative data, and work rigorously in a research environment.

Research area: Recent research and teaching areas primarily focus on the system analysis and dynamic modelling in heterogeneous cyber-physical-social interaction, agentic AI and semantics enhanced cognitive computing and data analytics in decentralized real-time control and monitoring applications for environmental and engineering computing systems, and smart eLearning systems.

Student roles:
Students are required to conduct the following task analysis, functions and structure design, and implementation as an active and reflective learner.

• abstract and represent the interior design knowledge with multimodal knowledge graphs
• use Neo4j or alternative to build knowledge graphs to engage with human’s creative interior design and pre-construction tasks and decision making.
• use knowledge graphs to engage with agentic AI for constraint-grounded and efficient execution towards engineering-focused computation.
• analyze an application case to augment the created KGs over the interior design scenario
• analyze how the KGs-based approach impacts on the performance of human-AI collaboration for the interior design and pre-construction tasks

Skills required:
A student taking on this project requires an interdisciplinary background combining Artificial Intelligence (AI), computer programming, design/architecture, and a specific blend of technical and domain skills

Research area: Dr. Abdulrazak research interests include Ambient-Intelligence (AmI), IoT, Smart-Homes, Smart-Cities and Digital health. He works on various R&D projects at the AMI laboratory. The projects range from Assistive Home Environment for the aging people, Electronic Organizer, Pervasive Reminder System, Mobility Assistance, Smart cities for active aging, etc. These technologies aim at being used either inside or outside (city, home, retirement communities, community clinics, on the street, etc.)

Student roles:
The selected intern will play a critical role in enhancing the reliability and scalability of our urban IoT infrastructure. The student will primarily focus on designing and implementing an automated, intelligent fault-tolerance and system analysis solution.
Key responsibilities and expected deliverables include:
• Architecture & Prototype Design: Designing a system health monitoring architecture tailored for vast, heterogeneous Smart City deployments.
• Prototype Implementation: Developing a functional software prototype capable of real-time tracking, diagnostics, and management of the remote physical IoT devices deployed throughout Sherbrooke.
• Data & Log Analysis: Utilizing data processing and machine learning techniques to detect anomalies, predict device failures, and automate fault-recovery protocols.
• Containerized Deployment: Leveraging Docker and Linux environments to package and integrate the analysis tool into the AMI-Lab's existing Big Data architecture.
Through this internship, the student will transition from theoretical concepts to practical, large-scale systems engineering. They will gain highly sought-after expertise in Big Data workflows, containerized architectures, and real-world IoT monitoring—skills that carry immense value in both industrial R&D and advanced graduate studies—while contributing directly to impactful, publishable smart city research.

Skills required:
Core Qualifications:
• Research Drive: Strong motivation to engage with complex, hands-on R&D challenges within real-world smart environment infrastructures.
• Programming Foundations: High proficiency in core programming methodologies and clean code practices.
Recommended Technical Assets:
• Foundational experience operating within Linux environments.
• Practical understanding of Web Services and API communication paradigms.
• Familiarity with containerization technologies, specifically Docker, for application deployment.
• Basic curiosity or background in Machine Learning, Big Data pipelines, or NoSQL databases.

Research area: My research intersects environmental data science and climate informatics. We focus on developing computational tools to harvest, process, and analyze large-scale environmental datasets. By leveraging programming and machine learning, we aim to translate raw, fragmented climate data into actionable insights, ultimately supporting urban planners, policymakers, and researchers in developing robust climate adaptation and resilience strategies.

Student roles:
The student will lead the software development and data engineering aspects of this project. During the first few weeks, they will conduct a technical review to identify key climate web archives and determine the most efficient extraction methods (APIs vs. web scraping). Their primary role will be to write, test, and debug Python scripts to automate the data retrieval process. Following data collection, the student will write data-cleaning pipelines to handle missing values, standardize units, and classify the information into specific climate adaptation categories (e.g., flood risk, heatwaves). The student will be expected to maintain clean, well-documented code via a version control system like Git. Throughout the internship, they will meet weekly with the supervisory team to discuss technical roadblocks, present data visualizations of their progress, and refine their extraction algorithms. By the end of the project, the student will deliver a fully functional codebase, a structured climate dataset, and a final technical report detailing their methodology.

Skills required:
The ideal candidate should be pursuing a degree in Computer Science, Data Science, Civil Engineering, Environmental Engineering, Geography or Environmental Science with a strong quantitative focus. Proficiency in Python programming is required, particularly experience with data manipulation libraries (Pandas, NumPy). Familiarity with web scraping tools (BeautifulSoup, APIs, Requests) and basic database management is highly desirable. A baseline understanding of climate science or environmental datasets is considered a strong asset, along with excellent problem-solving skills and the ability to work independently on coding tasks.

Research area: Software Engineering Applied Machine Learning for Software Engineering

Student roles:
The student will lead the project with the help of the supervisor. The student will conceptualize the architecture of the proposed agent and implement and test the software components of the agent. The supervisor will meet the student regularly and provide necessary guidance and feedback.

Skills required:
- Machine learning concepts, skills to develop and train a model
- Proficiency in Python
- Software engineering concepts and hands-on experimence

Research area: My research interests include software testing, automated bug fixing, ethically sourced code generation, and software ethics.

Student roles:
The student will perform literature review to study existing modernization techniques (refactoring, re-platforming), dataset selection, build or use AI-based tools for:
code analysis, dependency mapping, refactoring suggestions, implementation, and apply modernization strategies.

Skills required:
The student should have a solid foundation in computer science or software engineering, including proficiency in programming languages such as Java, Python, or C++. A good understanding of software engineering principles—particularly object-oriented design, data structures, and algorithms—is essential.

Experience with version control systems (e.g., Git), testing frameworks will be beneficial. Exposure to static code analysis tools, refactoring techniques, or software maintenance practices is an asset. Additionally, some background in machine learning or data analysis would be advantageous for exploring automated modernization approaches.

Research area: Software Engineering Applied Machine Learning for Software Engineering

Student roles:
The student will lead the project with the help of the supervisor. The student will conceptualize the architecture of the proposed agent and implement and test the software components of the agent. The supervisor will meet the student regularly and provide necessary guidance and feedback.

Skills required:
- Machine learning concepts, skills to develop and train a model
- Proficiency in Python
- Software engineering concepts and hands-on experimence

Research area: My specialized research interests lie in Software Engineering and Software Quality. Through my research, I aim to facilitate software development, maintenance, and evolution using machine learning and develop new methods and techniques. More specifically, I focus on analyzing software artifacts (e.g., design, source code, documentation, requirements, etc.) using machine learning and natural language processing techniques, assessing the quality of software design and implementation, analyzing and prioritizing software requirements, and managing technical debts or issues.

Student roles:
- Write an NLP parser to parse natural language (Python)
- Extract critical information from the user stories
- Derive functional and non-functional requirements
- Validate the findings using the developers survey
- Write a final report with research questions answered

Skills required:
- Strong programming skills in Python
- Natural language parsing and processing
- At least NLP library in Python (NLTK, CoreNLP, or scikit-learn)
- Basic knowledge of SQL databases and queries

Research area: Dr. Pawan Lingras’ research spans artificial intelligence, machine learning, and data mining, with a focus on soft computing techniques such as rough sets, fuzzy sets, and neural networks. His work emphasizes clustering, classification, and prediction, applied across diverse domains including healthcare, finance, retail, and engineering. He is particularly interested in MLOps, explainable AI, and adaptive learning systems. With over 250 publications and extensive industry collaboration, his research bridges theoretical innovation and real-world impact. Dr. Lingras also explores the integration of AI in digital health platforms, smart infrastructure, and educational analytics, fostering interdisciplinary and applied research excellence.

Student roles:
The student will be responsible for the end-to-end development of GenAI and Agentic AI solutions to automate SME healthcare processes. This role involves conducting business analyses of time-consuming tasks, such as drug dosage calculations and surgical readiness audits, to identify automation opportunities. The student will identify, test, and implement the most effective LLMs to generate medical reports from sonography screenshots and voice recordings. Additionally, they will develop an autonomous reasoning layer to "pull" real-time lab results and "browse" national health databases. Finally, the student will collaborate with medical staff to design and test UI/UX interfaces that reduce cognitive load.

Skills required:
The student must possess a strong background in Natural Language Processing and Machine Learning to identify and test the best LLMs for medical report generation. Proficiency in Python is essential to develop Agentic AI capable of autonomously pulling EHR data and browsing national health databases. The role requires data engineering skills to curate training datasets from sonography screenshots and voice annotations. Additionally, the student needs business analysis and UI/UX design skills to audit manual workflows and create intuitive interfaces in consultation with healthcare personnel.

Research area: 6G, the latest telecommunication standard, aims to integrate digital twins into future networks for applications like predictive maintenance and "what-if" scenario evaluation. A digital twin is a virtual model of a system used to simulate conditions without affecting the real system. However, the complexity of 6G networks makes creating a comprehensive digital twin challenging. This project aims to automate the design of digital twins for 6G networks using tools like OpenAIR Interface and Qemu, targeting scenarios such as component failures, upgrade issues, and maintenance-related disruptions.

Student roles:
1) Review the state of the art on digital twins for 6G
2) Identify the tools that can potentially be used to achieve the project's objective
3) Development and integration efforts to achieve the objective of the project

Skills required:
1) Background on telecommunication networks
2) Background on automation tools
3) Familiarity with openAIR Interface and Qemu

Research area: My research focuses on artificial intelligence for cybersecurity, autonomous cyber defense, large language models, multi-agent systems, and intelligent threat detection. I develop AI-driven approaches that automate threat hunting, attack detection, incident analysis, and response activities. The research combines machine learning, knowledge graphs, cybersecurity analytics, and autonomous decision-making to improve cyber resilience and support security operations in complex digital environments.

Student roles:
The student will conduct literature reviews, develop AI-based cybersecurity models, analyze cybersecurity datasets, and evaluate system performance. Responsibilities include implementing machine learning algorithms, testing autonomous agent frameworks, generating visualizations, and documenting experimental results.

The student will participate in regular research meetings and collaborate with graduate students and researchers. They will contribute to technical reports, presentations, and potentially academic publications. The internship provides practical exposure to AI-driven cybersecurity research and advanced threat detection methodologies.

Skills required:
Students should have a background in Computer Science, Cybersecurity, Artificial Intelligence, Software Engineering, or Information Technology. Experience with Python programming is required. Basic knowledge of machine learning, networking, or cybersecurity concepts is desirable. Familiarity with data analysis and programming tools is beneficial but not mandatory.

Research area: My research focuses on robotics, artificial intelligence, and autonomous systems, with expertise in embodied AI, human–robot interaction, and intelligent robotic systems. I develop autonomous robots using machine learning, deep learning, computer vision, and control systems for perception, decision-making, and navigation in complex environments. My research includes Large Language Models (LLMs), Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, SLAM, and reinforcement learning for mobile robots, drones, and humanoids. Applications include precision agriculture, industrial automation, assistive robotics, autonomous vehicles, and smart manufacturing, with the goal of creating adaptive and intelligent systems that improve productivity and quality of life.

Student roles:
The student will contribute to the design, implementation, and testing of autonomous robotic systems for navigation in GPS-denied environments. Responsibilities include developing software for perception, localization, mapping, and path planning using ROS/ROS2 and AI techniques. The student will integrate and calibrate sensors such as LiDAR, cameras, and IMUs, conduct experiments on drone or robotic platforms, analyze experimental data, and document research findings. The student is also expected to participate in team meetings, collaborate with researchers, and contribute to publications and technical reports related to autonomous systems and embodied AI.

Skills required:
The ideal student should have a background in robotics, mechatronics, computer science, electrical engineering, or a related field. The student should possess knowledge of programming languages such as Python, C++, or MATLAB, and have familiarity with ROS/ROS2, Linux, and embedded systems. Experience with machine learning, computer vision, SLAM, sensor fusion, and autonomous systems is highly desirable. Hands-on experience with robotic platforms, drones, LiDAR, cameras, and IMU sensors is an asset. The student should demonstrate strong analytical, problem-solving, and teamwork skills, along with an interest in artificial intelligence, autonomous navigation, and embodied robotic systems.

Research area: My research focuses on empirical software engineering for video games, with emphasis on automating gameplay quality assurance (QA). I integrate deep reinforcement learning (DRL), large language models (LLMs), and software testing to build open-source tools that produce reliable, interpretable evidence of game defects, imbalance, and pacing issues. My work targets resource-constrained studios that make up roughly 99% of releases on Steam. Research outcomes include modular playtesting architectures for Unity and Godot, persona-driven testing agents, and reusable metric abstractions for fairness, pacing, and engagement. The broader goal is to scale automated QA across game genres while remaining accessible to small studios.

Student roles:
The intern will join an active research program on automated game QA and take ownership of a defined experimental track within the larger initiative. Their work will progress through four phases over the 12-week internship.

In weeks 1–2, the intern will onboard with the team and set up the development environment. They will familiarize themselves with the modular playtesting architecture and the existing codebase, then select two to three target games from the project's curated open-source benchmark suite.

In weeks 3–6, the intern will implement and train persona-driven agents, starting with a speedrunner persona as the baseline. They will instrument the games via the engine adapters, design reward signals for each persona, run training experiments using DRL algorithms, tune hyperparameters, monitor convergence, and log playtraces in the project's standardized JSON/CSV formats.

In weeks 7–10, the intern will evaluate agent transferability across procedurally generated levels, performing comparative experiments and statistical analysis of metrics including coverage, time-to-completion, and detected anomalies (softlocks, glitches, balance issues). They will produce visualizations and contribute analysis modules to the team's dashboard.

In weeks 11–12, the intern will finalize the experimental report, contribute documented code to the open-source repository, and assist in drafting a research paper. They will present results to the lab.

The intern will meet weekly with the supervisor, attend lab meetings, and collaborate with PhD and MSc students working on adjacent tasks. They will have access to local GPU resources and Compute Canada.

Skills required:
The student should have a strong computer science background with completed coursework in machine learning, particularly reinforcement learning (PPO or actor-critic methods). Proficient programming in Python is required, including familiarity with PyTorch or TensorFlow and RL libraries like Stable-Baselines3. Experience with game engines (Godot, Bevy, Raylib, Pygame) is a plus. The student should be comfortable with version control, software engineering practices, and reading academic literature. Prior exposure to video games as software systems, gameplay analytics, or playtesting research is a plus but not required.

Research area: I work in intelligent autonomous systems, with particular emphasis on systems that must operate in challenging environments, such as underwater or on the water's surface. I have over 30 years experience working in this space and have helped to develop a number of robot of autonomous and supervised systems for environments from nuclear power plants, contaminated crime scenes, to shallow (30m depth) aquatic environments.

Student roles:
Software development. First in terms of system simulation and then state estimation from visual targets. As the term progresses work will move to robots operating in the water on campus.

Skills required:
Interest in autonomous systems. Interest in computer vision

Research area: My areas of research are computer vision and machine learning for processing complex and unstructured data. In particular, the use of new approaches such as Deep Learning and multimodal imaging in the fields of security and biometrics, UAVs, medical imaging and intelligent robotics.

Student roles:
Reading of some documents related to the subject.
Developing vision and machine learning algorithms using existing libraries.
Writing documentation/report on the work done.

Skills required:
Programming (Python, Matlab, etc.)
Machine leaning or image processing are considered a plus

Research area: The project is under the scope of cybersecurity initiatives to improve the cyber resilience of avionic architectures, involving different scopes: IDSs, operating systems modules, hardware monitors, dynamic behaviours.

Student roles:
work with doctoral students supporting them in the creation of new modules, validation of existing modules, results collection and results analysis.

Skills required:
Embedded systems, understanding of operating systems and low level programming (C, assembly).

Research area: Dr. Alexandre (Sasha) Douplik has been a Professor and Head of the Photonics Group in the Physics Department at Toronto Metropolitan University (formerly Ryerson University) since 2011, and a Scientist at St. Michael’s Hospital, Toronto, specializing in Advanced Optical Biomedical Imaging and Laser Surgery and Therapy, author of more than 70 peer-reviewed papers, 55 conference papers, seven book chapters, and 18 patents. He was also a visiting researcher at the Okinawa Institute of Science and Technology (OIST) in Japan in 2018 and 2023. From 2008 to 2011, he was a contract (non-tenure-track) Research Professor/Habilitant at Nuremberg-Erlangen University, Germany.

Student roles:
Experimental work, data processing and analysis, report writing, and publication participation.

Skills required:
Mechanical Design, Optics, Physics, Engineering, Programming

Research area: My specialized research area focuses on data privacy and security, with an emphasis on digital privacy, usable privacy, and searchable encryption methodologies. My research aims to advance usable privacy by designing privacy‑preserving systems that are understandable, practical, and minimally disruptive to users, ensuring that strong privacy protections can be effectively adopted in real‑world computing environments without sacrificing usability or functionality.

Student roles:
The student will play an active and supportive role throughout all stages of the research project under close supervision. The student will assist with conducting a comprehensive literature review, including identifying relevant academic papers, summarizing existing work on behavioural biometrics, data privacy, and usable privacy, and helping to position the project within the current research landscape.

The student will also support the research execution phase, contributing to data collection, analysis, and evaluation of behavioural interaction patterns related to user identification and privacy risks. As part of the technical component, the student will assist in the design of the research framework, helping to translate privacy and usability requirements into a structured system model.

In addition, the student will contribute to the implementation of a prototype tool, using their programming skills (e.g., Java or similar languages) to support development, testing, and refinement. The student will document findings, assist in preparing reports, and participate in regular discussions to reflect on research progress and results.

Skills required:
Ability to help with literature reviews
Strong interest in research
Familiarity with programming languages, such as Java or similar
Self‑motivation and willingness to learn
Basic understanding of data privacy

Research area: My research specializes in software engineering, with a focus on applying AI and data-driven techniques to improve software development processes. I work on mining software repositories, optimizing continuous integration and delivery (CI/CD), and leveraging large language models (LLMs) for tasks like code generation, testing, and configuration migration.

Student roles:
- Clean, filter, and organize the CI/CD pipeline dataset (deduplication, schema validation, anonymization)
- Implement evaluation scripts for benchmark tasks (e.g., build outcome prediction, duration estimation)
- Run and document baseline experiments (XGBoost, CodeBERT, GPT-4 few-shot)
- Fine-tune pre-trained models on benchmark tasks and record results
- Produce result tables, figures, and statistical summaries for the paper
- Write sections of the technical report and contribute to the paper draft

Skills required:
- Proficiency in Python (data processing, scripting)
- Familiarity with machine learning concepts (classification, regression, model training)
- Basic experience with HuggingFace Transformers or similar ML libraries
- Comfort working in Linux/command-line environments
- Basic understanding of Git and GitHub
- Ability to read and understand research papers
- Knowledge of YAML or similar configuration file formats is a plus, but not required

Research area: We work at the intersection of machine learning, wearable sensors and software engineering for health applications extracting meaningful insights from continuous physiological and behavioral data. A core challenge we address is bridging the considerable gap between controlled laboratory environments and the complexities of real-life data. We study data variability, temporal patterns, and how individual differences in activity patterns or physiological responses impact data interpretation. We aim to design wearable health systems that are truly robust, reliable, and sustainable for widespread adoption, paving the way for truly personalized and preventable healthcare.

Student roles:
The primary responsibilities will include setting up experimental pipelines for evaluating diverse ML/DL models, pre-processing specific datasets (a mix of public and internal) for analysis, adapting state-of-the-art and novel foundation models for time series to wearable sensor data, running extensive computational experiments, and analyzing statistical differences in model performance. They will also be responsible for debugging models and visualizing complex results to extract meaningful insights.
The intern will be mentored closely by the supervisor and graduate students, gaining hands-on experience and guidance. This internship offers significant learning opportunities, including deepening their understanding of machine learning and deep learning for time series data, mastering robust experimental design and benchmarking methodologies.
The lab provides access to necessary datasets, existing model implementations, relevant scientific literature, and high-performance computational resources. While students need to bring their own laptops, the heavy computation will leverage lab infrastructure. Progress will be regularly evaluated through code reviews and monthly presentations. The intern will be strongly encouraged to present their findings at internal lab meetings, and depending on the depth and impact of their work, there is a potential for co-authorship on a technical report or a research paper summarizing the benchmarking results and their implications.

Skills required:
Strong programming skills
Experience with machine learning libraries such as scikit-learn or tensor-flow even if small
Good oral/written communication
Ability to learn and work independently
Passion for solving problems

Research area: Signal and Image Processing Computer Vision Artificial Intelligence

Student roles:
- Study the fundamentals of quantum machine learning: Data encoding strategies (amplitude encoding, angle encoding), Parameterized quantum circuits (PQC), and Variational optimization.
- Select benchmark classification datasets: Iris, Breast Cancer (UCI), MNIST, Wine Quality, Credit Card Fraud Detection.
- Implement classical deep learning architectures: a CNN and a fully connected neural network (MLP) using PyTorch or TensorFlow.
- Build a parameterized variational quantum classifier (VQC).
- Implement a Hardware-Efficient Ansatz (HEA) classifier with PennyLane.
- Collect classification metrics: Accuracy, F1-score, training time vs. circuit depth.
- Analysis and documentation, code to produce and visually represent the results.

Skills required:
- Linear algebra, Calculus and optimization, Probability and statistics.
- Quantum computing, Variational principle, Quantum gates and circuits, Noise and decoherence.
- Supervised machine learning, SVMs and kernel methods, Neural networks.
- Python and scientific libraries, Qiskit or PennyLane, Deep Learning framework (Pytorch, Keras ou Tensorflow).

Research area: Prof. Pientka's rsearch interest lies in developing a theoretical and practical foundation for building and reasoning about reliable safe software systems. To achieve this goal, Prof. Pientka combines theoretical research on the logical foundations of computer science in programming languages and verification with system building. The topics she is interested in include modal logics, type theory, functional programming, logical frameworks, and proof environments.

Student roles:
The student will generate benchmark terms from the developments done in the Twelf system (or Beluga system). Both systems provide an implementation of the logical framework LF. The goal is then to compare the timing for type checking LF terms in Twelf / Beluga and comparing the performance to the extracted , verified type checker.

Skills required:
Ideally the student should have a background in foundations of programming languages such as understanding the lambda-calculus and basic typing. The student also should understand typed functional programming (such as OCaml or SML or F#). A background in the proof assistant Rocq can be helpful but is not necessary.

Research area: Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set.

Student roles:
It will be required that the student first acquaint themselves with either CPLEX or GUROBI in a programming environment. Then, the student will have to solve available instances of the quadratic knapsack problem, first in their direct quadratic form, then in their linearized form. The sizes of the instances will be increased gradually until the solvers are no longer able to produce a solution beyond a reasonable time limit. Finally, a thorough analysis of this experiment will be provided.

Skills required:
Linear algebra
Computer programming ( the programming language is not important)

Research area: My team works in Artificial Intelligence and Machine Learning. Specifically, we focus on Deep Learning, Reinforcement Learning, Lifelong Learning, Optimization, Foundation Models, and AI for Science. For more details, please check out the lab website: https://chandar-lab.github.io/

Student roles:
Study existing methods to mitigate loss of plasticity; implement existing methods and benchmark them; design new optimization methods to tackle loss of plasticity; implement and benchmark the newly proposed algorithm; write a report.

Skills required:
Strong background in deep learning and continual learning; strong coding skills; proficiency in Python and PyTorch; knowledge of multi-GPU and distributed training.

Research area: Dr. Stuerzlinger is a leading researcher in human-computer interaction and virtual, augmented & extended reality (VR/AR/XR). He also has deep expertise in computer graphics and high-performance systems. Dr. Stuerzlinger's lab has created several patented technologies and is actively exploring many new software and hardware solutions to improve user interfaces. Some of the outcomes of Dr. Stuerzlinger's work on better user interfaces for autocorrect have been adopted by Google and Apple. Graduates occupy a wide range of positions, including faculty members, CTOs, several CEO's, startup founders, software and user interface leads, and other leading positions.

Student roles:
The work within this project will happen under the direct supervision of Dr. Stuerzlinger. A part of the work may be performed in collaboration with other students or postdoctoral researchers in the lab. Students working on this project will implement a prototype of one or more novel interaction method(s) to deal with occasionally failing technologies, e.g., by implementing new algorithms or potentially creating new machine learning systems. Students may also perform a user study to evaluate the usability and performance of the prototype, supported by the research team. Finally, students will be involved in the dissemination of the results through co-authorship in scientific publications. Projects will involve the latest-generation input and output technologies.

Skills required:
Required:
- Excellent coding skills, ideally in JavaScript
- LLM experience
- Very good English language skills (speaking, reading, and writing)

Desired:
- Web development (JavaScript, servers, etc.)
- Human-Computer Interaction, Cognitive Science, or equivalent course
- Research experience
- iPhone or Android development
- Good presentation skills

Research area: Our lab investigates novel input techniques for wearable devices, with a focus on using onboard microphones as interaction sensors beyond standard voice command recognition. We explore how the smartwatch microphone can detect subtle non-speech acoustic events as a lightweight, socially unobtrusive interaction vocabulary. Our work combines acoustic signal processing, machine learning for audio event classification, and HCI evaluation to design audio-based wearable interaction systems that work without speech, internet connectivity, or a wake word.

Student roles:
In weeks 1–2, the intern will review literature on acoustic interaction, non-speech audio input, and wearable gesture recognition, and evaluate candidate sounds for social acceptability and acoustic distinctiveness. In weeks 3–5, the intern will design the acoustic gesture vocabulary, develop a data collection app using the WearOS microphone API, and run collection sessions with participants in both quiet and noisy conditions. In weeks 6–8, the intern will extract audio features, train and compare classifiers, and optimize the best model for on-device inference speed on the watch. In weeks 9–10, the intern will implement the prototype application mapping recognized gestures to watch commands and integrate the classifier on-device. In weeks 11–12, the intern will run the user study evaluating accuracy, learnability, and social acceptability, analyze results, and write a final report. The intern meets weekly with the supervisor and contributes to a paper draft targeting CHI or UIST.

Skills required:
Background in computer science, electrical engineering, or signal processing. Python for audio signal processing and machine learning is required. Familiarity with audio feature extraction. Experience with Android/WearOS development for on-device model deployment is a strong asset. Interest in novel interaction techniques, wearable computing, and HCI evaluation.

Research area: My sports analytics research focuses on quantification of strategy and player actions. I am especially interested in the usage of the playing area, such as shot locations in hockey or pass completions in soccer.

Student roles:
1. Accessing and familiarization with the data (the sport is chosen by the student).
2. Summarising the events that happen within each grid cell and each pair of cells into a network with information stored at each node.
3. Development of algorithms to simplify the network based on the nodes and their relationships.
4. Document and share the algorithm in an open source repository.
5. Demonstrate the utility of the network using pre-existing or new sports analytics methods.
6. Communicate the results in a technical report.

Skills required:
- Programming skills in either Python or R.
- Experience with developing and implementing algorithms in code.
- Some knowledge of mathematical graphs is preferred.
- Ability to communicate technical ideas to a broader knowledge.
- Statistical modelling is preferred.
- Familiarity with code sharing platforms such as git/GitHub is preferred.

Research area: My research focuses on the development of advanced artificial intelligence (AI), machine learning (ML), and evolutionary optimization methods for solving complex real-world problems, with a particular emphasis on healthcare applications. A major component of my work is computational pathology, where machine learning and deep learning models are used to analyze large-scale histopathology images for disease diagnosis, prognosis, and biomarker discovery. My research also investigates trustworthy and responsible AI, including model interpretability, fairness, bias analysis, and robustness.

Student roles:
The student will play an active role in a research project investigating bias and fairness in machine learning models for computational pathology. The primary objective is to assess whether state-of-the-art artificial intelligence models perform consistently across different patient populations, disease subtypes, healthcare institutions, and image acquisition conditions.
The student will begin by conducting a literature review on computational pathology, machine learning fairness, and bias evaluation methodologies. They will assist in collecting, organizing, and preprocessing large-scale histopathology image datasets and associated clinical information. The student will implement and evaluate machine learning and deep learning models using Python and modern AI frameworks such as PyTorch or TensorFlow.
A major component of the project involves designing and conducting experiments to measure model performance across different subgroups, identifying potential sources of bias, and analyzing factors that may affect model reliability and generalizability. The student will apply statistical and computational methods to interpret results and compare findings with existing studies in the literature.
The student will participate in regular research meetings, present progress updates, and collaborate with other members of the research team. They will maintain clear documentation of experiments, results, and code to ensure reproducibility. Depending on the project's progress and the student's interests, there may also be opportunities to explore bias mitigation techniques and contribute to the development of more equitable and trustworthy AI systems.
By the end of the internship, the student is expected to have gained practical experience in machine learning, computational pathology, scientific research, and data analysis. They will contribute to technical reports, conference presentations, and potentially peer-reviewed publications arising from the project. This role provides valuable exposure to interdisciplinary research at the intersection of artificial intelligence, healthcare, and responsible AI.

Skills required:
Applicants should have a background in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field. Strong Python programming skills are required, along with familiarity with machine learning libraries such as Scikit-learn, PyTorch, or TensorFlow. Students should understand fundamental concepts in machine learning, data analysis, and statistics. Experience with deep learning, computer vision, or medical image analysis is an asset but not mandatory. The project requires strong analytical and problem-solving abilities, attention to detail, and the ability to read scientific literature. Motivated students interested in AI for healthcare are encouraged to apply.

Research area: My research falls in the broad areas of AI, ML, and Robotics, and specifically it focuses on interactive machine learning. The goal of my research is to develop personalized autonomous systems that can continually interact with and learn about their users’ individual preferences and unique environments to provide personalized assistance to their users over the long term. Currently, I am interested in working on challenges associated with interactive continual learning both from the ML and human-robot interaction (HRI) perspectives to improve scalability, efficiency, and explainability of ML methods applied to robotics and autonomous vehicles.

Student roles:
The student will mainly be involved in learning and implementing active learning (AL) methods integrated with large language models (LLMs) in Python/Pytorch. They will get hands-on experience with state-of-the-art research in ML, and robotics to improve robots' personalization capabilities in dynamic unstructured environments. The student will meet with me once every week to go over the project objectives and progress. I will answer questions about the project and guide them to learn about the new technology in AL. The student will develop
various multi and interdisciplinary skills related to AI, and ML. Specifically, they will learn SOTA research in AL and adaptive AI.

Skills required:
1) Strong fundamentals in computer programming (preferably Python).
2) Motivation to learn new tools and research methods in AI.
3) Background in software engineering is a plus.
4) Background in AI (through courses or projects) is a plus, but not required.
5) Some experience with Pytorch is a plus, but not required.
6) Knowledge of Linux based systems is a plus, but not required
7) Knowledge of version control (git: GitHub, Gitlab) is a plus, but not required

Research area: Our laboratory bridges the gap between academic innovation and real-world adoption across trustworthy technology, security operations, and human readiness. On the technological dimension, we study how cybersecurity models scale within high-throughput, enterprise infrastructures. Our current research investigates the systemic limits of traditional machine learning and anomaly detection algorithms when exposed to actual Big Data environments. Rather than relying on curated, low-volume datasets that skew experimental accuracy, we evaluate system resilience against massive, raw telemetry streams. The objective is to identify algorithmic breakdown points and engineer resilient, high-capacity methods that practitioners can deploy seamlessly within live, high-velocity defensive pipelines.

Student roles:
The selected intern will function as a Big Data Engineer and Security Analytics Researcher, taking full ownership of building our algorithmic stress-testing sandbox. Over the 12-week internship, the student will drive an intensive, empirical research and engineering lifecycle:

Initially, the student will architect the data ingestion pipeline, importing and parsing massive, multi-gigabyte cybersecurity datasets into a distributed storage format (like Apache Parquet). They will then implement a suite of baseline machine learning detection algorithms. The first major technical milestone involves designing and running the "break-test" experiments—simulating high-velocity network traffic streams to measure and map out performance bottlenecks, memory usage limits, and accuracy degradation points.

Upon identifying these failure thresholds, the student will pivot to developing and testing optimized alternatives. They will write the code to adapt these algorithms for distributed processing environments, leveraging frameworks like PySpark or Dask to handle feature extraction and anomaly classification concurrently across large data matrices. They will mathematically validate that their proposed methods yield a significant reduction in computational latency while stabilizing false-positive rates.

In alignment with our lab’s emphasis on research entrepreneurship, the student will package their code into a highly polished, open-source benchmarking engine. This requires creating modular configurations (Docker), documenting data preparation pipelines, and building clean API endpoints for telemetry collection. The student will present data visualizations of their computational performance metrics during weekly laboratory briefings.

Skills required:
Applicants must be pursuing a degree in Computer Science, Data Science, or Software Engineering with a strong focus on data engineering or distributed computing. Advanced proficiency in Python and solid experience with Big Data analytics frameworks (such as Apache Spark, PySpark, or Dask) are required. Familiarity with machine learning libraries (Scikit-learn, PyTorch) and working with large structured/unstructured log files (PCAP, Parquet, JSON) is highly desirable. We seek performance-focused, systems-oriented students who are excited to work with large-scale storage arrays, optimize runtime computational efficiency, and bridge the gap between abstract algorithms and enterprise-scale data infrastructure.

Research area: My specialized research area in computer science relates to data management and data mining. In particular, my research team focuses on (a) data science, (b) data mining and analytics, (c) visualization and visual analytics, as well as (d) social network analysis and mining in recent years. Similar to the Mitacs Globalink Research Interns (GRIs) in summers 2013-2026, the student participates in this Globalink 2027 project will learn knowledge discovery and data mining techniques to provide a data science or business intelligence solution for Big data mining and analytics. Recent research outcomes can be found in https://sites.google.com/site/dblabuofm/publications

Student roles:
Research within the Database and Data Mining Laboratory in Department of Computer Science at University of Manitoba mainly focuses on databases and data mining, which includes efficient and effective management of, knowledge discovery from, as well as analysis of, large amounts of data (such as transactional, uncertain, social media, Web, biomedical & healthcare data, and/or streams of data, as well as Big data). My current research programs focus on data mining as well as applications of data mining technologies to social computing and/or social network analysis. Our lab is a multi-cultural environment. Current and previous lab members include students from Asia (e.g., Bangladesh, China, India, Japan, Malaysia, South Korea), Central and South Americas (e.g., Brazil, Mexico, Paraguay), and Africa (e.g., Tunisia). Current and previous collaborators include researchers from Australia and France. Following our success in the Globalink 2013-2026 projects with GRIs (who have published their research findings from their Globalink research with me in refereed international conferences/journals & who are now graduate students in Canadian/US universities such as UBC, Waterloo, Columbia, CMU & UIUC or working in companies such as Google), the student participating in this MITACS Globalink 2027 project will work under the same supervision model. In other words, the student will academic supervised by me (a Full Professor at the University of Manitoba) for an approximate 12-week research internship in the summer (with a flexible start date in May, June, or July). In addition, the student will also work closely with a senior lab member (who will serve as a mentor) and will contribute a small concrete data science component of the project suitable at the level for the undergraduate students.

Skills required:
Similar to skill requirements for Globalink 2013-2026 GRIs (now in top Canadian/US universities like UBC, Waterloo, Columbia, CMU & UIUC or companies like Google), students participating in this 2027 project are expected to have taken some third-year computer science courses (e.g., database courses) when they started the internship. Specific details of the project will be finalized after discussion and mutual agreement between the professor and student. In general, the student will be asked to design/implement in C/C++, Java, Python, R, Scala (or others), and may use Jupyter, a data science/analytics component that is suitable and manageable for an undergraduate student.

Research area: The supervisor's work sits at the intersection of building physics, environmental engineering, and human physiological science. His recent research focuses on the development and application of advanced machine learning architectures that integrate physical knowledge, spatial structure, and temporal dynamics applied to complex engineering and scientific systems where data is sparse,

Student roles:
The student occupies a central and autonomous role in this project. They are not a support technician, they are the main researcher responsible for the scientific output working under the guidance of two supervisors (one thermal engineering expert, one ML expert). Below the main requirements and the proposed timeline.

Requirements :
1. Define a systematic, ML-compatible parametric representation of multiple classes of bio-inspired fractal heat exchanger geometries
2. Apply Gaussian Process-based Bayesian Optimization to efficiently identify top-performing designs under individual performance criteria
3. Apply NSGA-III to generate the full Pareto frontier of optimal trade-off designs across conflicting objectives (heat transfer vs. pressure drop)
4. Train a Deep Reinforcement Learning agent to discover optimal dynamic operating strategies that maximize thermal efficiency under time-varying load conditions
5. Benchmark the AI-driven optimization results against classical methods and existing literature designs

Timeline (12 weeks) :
weeks 1–2: Literature review
weeks 3–5: Develop and validate ML model
weeks 5–8: BO implementation; single-objective exploration; top candidate identification per geometry family
weeks 9–10: PPO agent design; environment setup with surrogate; training and policy validation
weeks 11–12: Writing / publication

Skills required:
The ideal candidate holds a degree in Mathematics, computer or data science with solid proficiency in scientific machine learning or deep learning, Applied mathematics / numerical methods and scientific computing (ex : CFD).

Research area: The supervisor's work sits at the intersection of building physics, environmental engineering, and human physiological science. His recent research focuses on the development and application of advanced machine learning architectures that integrate physical knowledge, spatial structure, and temporal dynamics applied to complex engineering and scientific systems where data is sparse,

Student roles:
The student occupies a central and autonomous role in this project. They are not a support technician, they are the main researcher responsible for the scientific output working under the guidance of two supervisors (one thermal engineering expert, one ML expert). Below the main requirements and the proposed timeline.

Requirements :
1. Define a systematic, ML-compatible parametric representation of multiple classes of bio-inspired fractal heat exchanger geometries
2. Generate a large, structured multi-fidelity simulation database (low-fidelity + high-fidelity CFD) covering the full design parameter space
3. Develop and train a Physics-Informed Neural Network (PINN) that embeds governing transport equations to predict full thermo-hydraulic fields
4. Develop a Multi-Fidelity Graph Neural Network that leverages the tree-like topology of fractal channels for fast and accurate field prediction
5. Develop a Deep Operator Network for geometry-agnostic thermal inference, generalizing to unseen fractal geometries without retraining
6. Deliver a fully validated, packaged surrogate model

Timeline (12 weeks) :
weeks 1–2: Literature review
weeks 3–5: CFD campaign
weeks 5–8: PINN development and training; MF-GNN architecture design and training; DeepONet training
weeks 9–10: Cross-validation; leave-one-geometry-out tests; experimental blind test; benchmark comparison
weeks 11–12: Writing / publication

Skills required:
The ideal candidate holds a degree in computer or data science with solid proficiency in scientific machine learning or deep learning, Applied mathematics / numerical methods and scientific computing (ex : CFD).

Research area: Our laboratory works on the development of personalized therapies in ovarian cancer. We employ longitudinal multi-omique analysis of clinical and pre-clinical samples to identify therapeutic vulnerabilities and therapeutic opportunities.

Student roles:
The student will be working with a database of single-cell proteomics and will build a new software that will be integrated to our cyclic-immunofluorescence platform.

Skills required:
Skills in coding is required. Python language knowledge is preferable.

Research area: This project proposes to create a durable, secure learning trace recording and analytics inferencing process for use in any environment which wants to record learning traces. As a priority, this system will record raw data and inferences in an immutable branching chain of learning events. In so doing, the resulting learning traces will be resistant to tampering and will be in a form amenable to encryption and controlled sharing.

Student roles:
First, the candiates will explore the problem and conduct literature review on blockchain and learning analytics. Then, they will then develop a prototype blockchain using existing freeware and populate it with data from an existing learning analytics system. Finally, they will then conduct tests on immutability of blockchains and explore the inferencing capacity of the learning analytics system with preexisting algorithms.

Skills required:
The candidates should have coding skills in Python or exposure to Claude coding environments.

Research area: I am a former professional game developer whose research group explores game design and development, human-computer interaction, games user research, AI, AR/MR/VR, and serious games. We are recruiting undergraduate interns for our upcoming project, "Blue Zone Bistro," an educational, narrative-driven management simulation and serious game. Through the journey of running a neighborhood cafe, the game teaches critical thinking about modern nutrition, digital-age food marketing, and long-term physical vitality. Players will learn to recognize and reject harmful dietary variables, including ultra-processed foods, deceptive chemical ingredient labels, and industrial food additives.

Student roles:
The project utilizes a "Divide and Conquer" strategy, splitting responsibilities into specialized disciplines while requiring close collaboration on design and playtesting.

Intern A: Lead Programmer & Systems Designer
This intern is responsible for the game’s technical architecture, logic, and state management. Core duties include:
*Developing the structural mechanics for the "Ingredient Inspector" label-scanning system.
*Implementing the data structures that track customer health metrics, progression systems, and "Blue Zone" unlocks.
*Coding the user interface functionality and ensuring responsive drag-and-drop systems.
*Managing the Git repository and handling final WebGL optimization for browser deployment.

Intern B: Lead Artist & Narrative Designer
This intern owns the visual identity, user experience, and educational storytelling of the game. Core duties include:
*Creating all 2D art assets, including environmental backgrounds, UI elements (food labels), and character sprites that visually transition from sluggish to energetic.
*Designing intuitive layouts for menus, the kitchen workspace, and tooltips.
*Writing engaging dialogue, tutorials, and integrating educational "pop-ups" that explain the real-world science behind processed foods and longevity zones.
*Managing sound design integration (background tracks and audio feedback).

Joint Responsibilities
Both interns will collaborate heavily during Weeks 1–3 to design the Minimum Viable Product (MVP) and map out the user flow. They will jointly run mid-project playtesting sessions, evaluate user feedback, and spend the final two weeks balancing the gameplay economy to ensure the educational message is seamlessly integrated, rewarding, and fun.

Skills required:
Applicants should be undergraduate students in Computer Science, Game Design, Digital Arts, or related fields. Experience with a 2D game engine (Unity, Unreal or Godot) and version control (Git) is highly recommended.

One intern should possess strong programming skills (C#/C++ or GDScript) and an interest in systems architecture and UI implementation. The other should have a background in 2D digital art (sprites, UI layout, animation) and creative writing. A shared interest in health sciences, nutrition, or public health education is a major asset for both roles.

Research area: My research program is dedicated to translating insights derived from non-invasive neuroimaging modalities, specifically electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI), into clinically relevant applications for brain disorders. Leveraging my dual affiliation as a scientist at Simon Fraser University and within local public hospitals, I explore how variability and pathological alterations in these brain signals manifest across diverse neurological populations and throughout typical development and ageing processes. A central aim is to elucidate the functional correlates of this neurophysiological variability, with the ultimate goal of developing and validating clinically translatable computational methods that enhance diagnostic accuracy.

Student roles:
The student intern's primary responsibility will be to analyze associations between patients’ age and electroencephalograms (EEGs) from an extensive clinical database. This will involve employing advanced statistical techniques (largely in Python), including predictive analytics and machine learning models, to identify correlations and potential predictive relationships. Key tasks include data preprocessing, feature extraction, model development, and validation of analytical pipelines.
A significant component of this role involves disseminating research outcomes. The intern will be expected to contribute substantially to, and potentially lead, the preparation of a research manuscript suitable for peer-reviewed publication. This will entail articulating the study's rationale, methodology, results, and their broader scientific implications.
Furthermore, the intern will actively collaborate with other students and researchers within the broader research initiative focused on clinical EEG and data science applications. This collaborative engagement is crucial for exchanging knowledge, addressing methodological challenges, and advancing the collective development of AI tools in clinical neurophysiology and healthcare.

Skills required:
The ideal candidate for this internship will possess a strong academic background in a quantitative discipline. This typically includes fields such as Mathematics, Statistics, Computer Science, Physics, Engineering, or a closely related area with a significant analytical component, providing the foundational knowledge necessary for complex data analysis and modeling.
Familiarity with Python is beneficial, as it will be the primary tool for data manipulation, analysis, and algorithm implementation within this project.
Furthermore, the candidate should have an understanding of statistical principles and techniques. Prior experience applying machine learning concepts, even in academic coursework or personal projects, would be advantageous.

Research area: My specialized research area focuses on data analytics, algorithm design, and information visualization, with applications in workforce intelligence and skill forecasting. I explore computational methods to analyze labor market trends, forecast future skill demands, and develop decision-support systems. My work also involves the application of knowledge graphs and machine learning to model employment patterns, enabling strategic planning in education and workforce development aligned with emerging technological transformations.

Student roles:
The student will play a critical role in supporting the research and development of a knowledge graph and recommendation system aimed at analyzing emerging skill demands across various sectors in Canada for 2030. Their responsibilities will include collecting and preprocessing data from labor market sources, national databases, and industry reports; conducting literature reviews on skill transition trends; assisting in the design and implementation of the knowledge graph structure; and contributing to data analysis and visualization. The student will also help validate results through stakeholder feedback and assist in drafting technical documentation and research summaries. Active participation in team meetings, collaboration with academic and industry mentors, and an interest in labor market analytics, AI/ML applications, and public policy will be expected.

Skills required:
The ideal student should have a foundational understanding of data science, including skills in data collection, cleaning, and analysis. Proficiency in programming languages such as Python or R is essential, along with familiarity in using data visualization tools (e.g., Tableau, Power BI, or Matplotlib). A basic knowledge of machine learning concepts and techniques, especially those related to recommendation systems or predictive modeling, is desirable. The student should be comfortable working with structured and unstructured datasets and possess strong analytical and problem-solving abilities. Familiarity with employment data, labor market trends, or economic forecasting will be considered an asset.

Research area: My research explores how people interact with intelligent technologies. I design and evaluate interactive systems that combine AI, visualization, computer vision, and extended reality technologies to support learning, decision-making, creativity, and collaboration. Students in my lab work on projects involving human-AI interaction, educational technologies, mixed-reality experiences, and novel user interfaces, with opportunities to build and evaluate real-world systems.

Student roles:
The students will:

- Develop software components for a mixed-reality assistance system.
- Implement and test interactive guidance features.
- Contribute to the design of user interfaces and user experiences.
- Assist with integrating AR/VR technologies and AI-driven functionality.
- Participate in prototype evaluation and data collection.
- Analyze results and contribute to research reports and presentations.
- Work closely with graduate students and faculty researchers in a collaborative research environment.

The student will gain hands-on experience in research, software development, human-computer interaction, and emerging technologies while contributing to an active research project with potential real-world industrial applications.

Skills required:
Applicants should have completed coursework in Computer Science, Software Engineering, Mechatronics, Human-Computer Interaction, Artificial Intelligence, or a related field. Experience with programming (e.g., Python, C#, JavaScript, Java, or C++) is required. Familiarity with Unity, AR/VR development, computer vision, robotics, or user interface design is an asset but not required. Students should be motivated, curious, and comfortable learning new technologies and working as part of a multidisciplinary research team.

Research area: I conduct research that explores new methods for making sense of data as it moves between physical and digital worlds. Recent research has focused on virtual reality games for spatial cognition, augmented and virtual reality environments for motor learning, immersive media for medical training, and data interactives for migration research. I approach these topics from the position that novel digital media, HCI, and data journalism present valuable opportunities to expand how training and insight generation can be undertaken. While data visualization is at the core of my research, I work with methods that are not constrained by dashboard development.

Student roles:
Student(s) will work directly with the supervisor and research team (spread across three universities in the Greater Toronto Area) to design, build, and evaluate data visualization products for translating insights from the MEMO research project. These will include custom-built geospatial dashboards, scrollytelling pages, data explainer videos, infographics, collaborative interfaces that include tangible and embodied interaction, and more. Students will explore cutting-edge methods for building data-driven interactives, and will implement them to develop prototypes for knowledge translation that may be used by researchers, policymakers, and the general public.

Skills required:
Student(s) should have an academic background in Design, HCI, Data Science, Information Visualization, Migration Studies, or a related field. They should possess basic working knowledge of visualization software (e.g. Tableau, Power BI, Flourish), GIS (e.g. ArcGIS, QGIS), visualization libraries for web development (e.g. D3, Observable, Mapbox GL), and the ability to work with R, Python, JavaScript, and web dev languages. Good graphic design skills and a portfolio of visualization projects will be valuable. The ability to read and interpret academic papers from HCI, data science, or Infovis will be valuable.

Research area: My research area is focused in wireless and mobile networks, and traffic forecasting and simulation, with emphasis on three sub-areas: 1) vehicular networks (connected cars, smart cities, etc.) 2) wireless sensor networks 3) Vehicular Traffic Forecasting and Simulation using Machine Learning I've been working with machine learning methods to improve vehicle traffic simulation. I am looking for students to work with machine learning, federated learning, reinforcement learning, data mining, and traffic simulators to improve accuracy.

Student roles:
- Design and implement components of a traffic forecasting system that predicts flow, speed, and congestion using real-world datasets
- Collect, clean, and preprocess data from multiple sources such as traffic sensors, weather reports, and incident logs
- Explore and analyze datasets to identify patterns, trends, and useful features for modeling
- Develop and evaluate machine learning or statistical models for short and long-term traffic prediction
- Integrate multiple input variables into a unified forecasting framework
- Contribute to the design and development of a traffic simulation tool for "what-if" scenario analysis (e.g., road closures, accidents, demand changes)
- Attend weekly research meetings, and report to the supervisor and/or graduate students involved in this project
- Assist in designing visualizations or dashboards to communicate model outputs and simulation results
- Continuously research and learn new techniques relevant to traffic modeling, machine learning, and smart city applications
- Contribute to a final report and/or presentation summarizing the project outcomes and impact

Skills required:
- Strong programming skills (preferably Python) for data processing and modeling
- Familiarity with libraries such as NumPy, pandas, and basic visualization tools (training can be provided)
- Understanding of data structures and algorithms for handling large datasets
- Basic knowledge of machine learning (e.g., regression, time series, neural networks) is helpful
- Interest in working with real-world, multi-source data (traffic, weather, incidents)
- Interest in simulation, modeling, or system design
- Bonus: front-end or visualization skills for interactive tools
- Strong problem-solving, teamwork, and willingness to learn

Research area: My research focuses on Digital Twins (DTs) which are virtual representations of physical systems such as manufacturing equipment. I want to make them easier to build, connect, and trust by capturing and formalizing domain knowledge. In particular, I want to make sure that DT components can be composed reliably and verified to behave correctly. My work spans DT engineering, architecture modelling, and the formal verification of software models. My team has recently been exploring LLMs for lowering the barrier to applying rigorous techniques, so that engineers can benefit from formal methods without extensive expertise. This project is the next step.

Student roles:
You'll join my team, which researches techniques for accelerated and trustworthy DT construction. The lab atmosphere is friendly and supportive. Montréal is one of the world's great student cities: bilingual, affordable, with a thriving arts and food scene and an exceptional place to spend a summer.

At least once a week, you'll meet with me to discuss project progress, and I'll help guide you to keep the project moving. Senior PhD students specializing in DT interoperability and our collaborators can also assist you where needed.

You'll take the lead in researching and implementing LLM-assisted contract generation for DT components, using DT case studies available in the lab. The project has three objectives:

- **O1:** Use an LLM to generate assume/guarantee contracts from DT component descriptions
- **O2:** Verify the generated contracts using a contract verification tool and iterate on the prompting strategy to improve correctness
- **O3:** Integrate the full pipeline into a small tool that takes a component description and outputs verified contracts

Each objective is scoped to roughly four weeks. DT case studies are available in the lab, so you can begin building and testing the pipeline from the start.

Skills required:
## Required Technical Skills

- Basic software engineering (Python, version control, etc.)
- Experience with LLMs or prompt engineering
- Interest in software correctness or reliable system composition

## Optional Technical Skills

- Formal methods, logic, or contract-based design (e.g. temporal logic, model checking)
- Digital Twins or cyber-physical systems

Curiosity and willingness to learn matter most.

Students should be motivated and able to work independently. A strong past project in a related area is an asset.

Research area: Prof. Pientka's research interest lies in developing a theoretical and practical foundation for building and reasoning about reliable safe software systems. To achieve this goal, I combine theoretical research on the logical foundations of computer science in programming languages and verification with system building. My work is grounded in the Curry-Howard isomorphism where we build elegant and robust foundations for the next generation of programming languages grounded in logic and type theory. The topics I am interested in include modal logics, type theory, functional programming, logical frameworks, and proof environments.

Student roles:
The student will build on our existing theoretical work that provides a logically grounded reconstrution of the Quipper programming language and its extensions. The student will implement this foundation in the Rocq proof assistant and implement the proofs that have mostly been done on paper (or in the proof environment Beluga). The goal is to implement in Rocq a reference implementation of the Proto-Quipper language that is proven correct and extracted to an OCaml program. If time permits, the student will use the framework to implement some basic quantum algorithms within this framework.

Skills required:
Ideally students have a background in typed functional programming and an understanding on how to define and reason about programming languages (for example at the level of the first 8 Chap of Types and Programming Languages by B. Pierce). Exposure to the proof assistant Rocq through for example Software Foundations is an asset.

Research area: Epilepsy, the most common chronic neurological disorder in children, is characterized by recurrent unprovoked seizures due to uncontrolled brain activity, over 30% of epilepsy cases do not respond to standard anti-seizure medications. Alternative treatments include surgery, neuromodulation, the ketogenic diet, and experimental therapies. I actively engage in investigator-driven studies and serve as the principal investigator on several clinical trials. My interests include identifying EEG findings as clinical predictors, exploring epilepsy-related psychiatric comorbidities, evaluating epilepsy surgery outcomes, and investigating innovative treatments for the condition.

Student roles:
We offer two opportunities for individuals interested in epilepsy research and treatment. The first role involves computational modelling. Participants will expand the Epileptor model to better represent the variety of seizure patterns observed in children with drug-resistant epilepsy. While no prior experience is required, a strong foundation in dynamical systems modelling and an interest in brain activity simulation are crucial assets. This project offers hands-on experience building and validating simulations that can help us understand how different seizure types emerge and identify effective treatment strategies through virtual models of the human brain.

The second opportunity focuses on the neurological treatment of epilepsy, where students will learn to evaluate potential treatments within BrainXplore's framework. This role provides a chance for those interested in applying neuroscience research to real-world clinical settings. Students will be trained to use BrainXplore to simulate different neuromodulation techniques such as TDCS and Temporal Interference. They'll learn how to translate simulation results into practical treatment recommendations, collaborating with clinicians along the way. This role allows students to gain experience evaluating the potential benefits and risks of various therapies and contribute to a better understanding of how we can improve epilepsy care for children facing drug-resistant seizures.

Mentors will work closely with each student, offering guidance throughout the internship. Each position offers a unique opportunity for hands-on research experience while learning from leading experts in the field.

Skills required:
Candidates should possess a solid foundation in computational neuroscience, demonstrated through experience analyzing brain imaging data from EEG, MRI, or DTI, and understanding neuroimaging software like Freesurfer, MNE, or MRTrix. Previous experience with dynamical systems theory is valuable, particularly when applied to brain activity modeling. Familiarity with neuromodulatory treatments such as TMS or DBS is a plus, although computational model building skills are also highly sought after. Passion for epilepsy research, especially in children with drug-resistant cases, is essential; candidates should be enthusiastic about contributing impactful findings in a collaborative environment.

Research area: My research sits at the intersection of cybersecurity and critical infrastructure, focusing on how complex systems can be made more resilient to disruption. My work has primarily examined energy systems, operational technologies, and other high-impact sectors. I am particularly interested in how technical vulnerabilities translate into operational and business risk, and how these risks can be effectively detected, managed, and communicated. More recently, I have been developing simulation-based training environments, including SOC/SIEM platforms, to bridge the gap between theory and practice and support more realistic, experience-driven approaches to cybersecurity and resilience training.

Student roles:
The student will be involved in the early scoping and design of the simulation platform, helping to shape how the tool functions and what features are included. This will involve translating business continuity concepts into interactive scenarios and thinking through how different decisions should be represented within the simulation.

They will also contribute to the development of the platform, primarily in Python, working on components such as the simulation logic, user interaction, and basic interface elements. The student will work closely with Dr. Higgins throughout the project, with regular discussions to refine ideas, review progress, and guide implementation.

In addition, the student will help test and iterate on the tool, ensuring that it is both technically functional and effective as an educational resource. This may include running trial scenarios, identifying areas for improvement, and making adjustments based on feedback. Overall, the role is hands-on and applied, with a balance between design, development, and practical problem-solving.

Skills required:
The ideal student will have some experience with programming or software development, with familiarity in Python being particularly useful. A background in computer science, engineering, or a related field would be beneficial, but students from other disciplines with relevant technical skills are also encouraged to apply. An interest in cybersecurity, systems design, or interactive applications would be helpful. The project will suit students who enjoy building practical tools, thinking through complex problems, and working in an applied, hands-on environment.

Research area: My research centers on computational statistics and complex probabilistic modeling, with a specific focus on developing advanced Bayesian inference and Monte Carlo methods for high-dimensional data analysis. A core component of my work involves explicitly integrating domain-specific knowledge into these statistical frameworks to produce highly accurate, context-aware algorithms. I am equally committed to translational research, engineering user-friendly software packages and web applications that democratize access to these sophisticated machine learning and inference tools for the broader scientific community.

Student roles:
Project Roles and Responsibilities:

1. Statistical Machine Learning Implementer: The student in this role will focus on the analytical core of the project, implementing statistical methods based on provided pseudocode. Strong proficiency in Python or R is required to build robust, scalable software packages.

2. Frontend Application Developer: This student will drive the user-facing development of the web and mobile applications. Using HTML, CSS, and JavaScript, they will design interactive and accessible interfaces to ensure the complex predictive models are usable by community stakeholders.

3. Backend Infrastructure Developer: This student will be responsible for the server-side architecture. They will configure the database, build secure APIs to handle data requests, and integrate the machine learning models into the web platform's backend ecosystem.

4. Data Analyst and Software Tester: This role bridges software validation and scientific research. The student will rigorously test the developed software packages and applications for bugs and edge cases. Subsequently, they will apply these tools to analyze targeted datasets and synthesize their findings into formal research reports.

Skills required:
I am seeking motivated students with a strong foundation in computer science, statistics, or software engineering. Because this is a highly interdisciplinary project, applicants can apply under one of two primary tracks:

Machine Learning & Backend Track: Requires proficiency in Python or R for statistical modeling and data analysis. Familiarity with database management (e.g., MySQL, PostgreSQL) and building backend APIs is highly desirable.

Web & Application Development Track: Requires strong programming skills in frontend web technologies (HTML/CSS, JavaScript/TypeScript). Experience with modern web frameworks (e.g., React, Vue) and UX/UI design principles is a major asset.

Research area: We focus on the study of engineering-relevant fluid flows that are characterized by a strong interaction between thermodynamics, acoustics and chemical kinetics using high-fidelity numerical simulations. Our research addresses problems in the fields of rocket propulsion, aerospace and novel energy production systems.

Student roles:
The student will collaborate with other team members to advance this project. The student will do simulations, conduct literature reviews, and post-process/visualize the resulting flow fields.

Skills required:
The student should have a strong computational background with advanced level of fluid mechanics/thermodynamics/heat transfer courses.

Research area: This project lies at the intersection of Artificial Intelligence (AI), neuromorphic engineering, CMOS integrated systems, MEMS sensing technologies, and wireless signal processing, targeting next-generation intelligent sensing platforms. It utilizes WiFi Channel State Information (CSI) as a privacy-preserving, contactless sensing method capable of capturing subtle human movements through wireless channel variations. The system integrates Spiking Neural Networks (SNNs), a neuromorphic AI approach that enables event-driven and energy-efficient computation. The research focuses on hardware–algorithm co-design, optimizing AI models not only for accuracy but also for low-power operation compatible with CMOS-based edge devices and potentially MEMS or RF front-end integration.

Student roles:
The student will be responsible for the end-to-end development of the CSI-based human activity recognition system. This includes collecting and organizing WiFi CSI datasets, performing signal preprocessing (denoising, normalization, and segmentation), and designing effective feature extraction or spike encoding methods suitable for neuromorphic processing. The student will implement and train Spiking Neural Networks (SNNs), exploring learning rules such as STDP and surrogate gradient backpropagation. They will also benchmark results against traditional machine learning and deep learning models. Additional responsibilities include optimizing model performance for low-latency and low-power inference, analyzing robustness under different environments, and documenting experimental results.

Skills required:
We are looking for a student with a strong background in computer engineering, or a related field, with solid knowledge of wireless communication systems. Understanding of radio propagation and metrics such as (CSI) is desirable. Prior experience in machine learning is an important asset. Familiarity with biologically inspired neural architectures such as (SNNs) is a plus but can be developed during the project. The candidate should have high programming skills (Python required, MATLAB preferred). Exposure to embedded systems or IoT platforms is also beneficial. Overall, we are seeking a motivated and independent student capable of working across interdisciplinary.

Research area: My research focuses on AI governance, responsible machine learning, sustainability, judgment and decision-making, and business and health analytics. I study how AI systems, including large language models, influence organizational decisions and how their outputs can be evaluated, audited, and governed. My work examines issues such as reliability, accountability, bias, advice quality, and responsible AI use in business and health contexts. I apply these questions to areas such as operations, accounting, auditing, corporate reporting, sustainability, EDIAB, and board and executive decision-making. Methodologically, my research combines experimental design, statistical modelling, simulation, machine learning, natural language processing, and computational text analysis.

Student roles:
The student will contribute to the design and implementation of a research project that evaluates the reliability of large language models (LLMs) as decision advisors. The role combines literature review, experimental design, Python-based implementation, data analysis, and research reporting.
The student’s responsibilities will include:
1) Literature review: The student will help identify, read, and summarize relevant research on LLM evaluation, responsible AI, decision bias, human-AI interaction, AI governance, and AI-assisted decision-making. The student will contribute to organizing the literature and identifying concepts that inform the experimental design.
2) Experimental design and scenario development: The student will help create decision scenarios that allow LLM-generated advice to be compared across equivalent formulations. This will include assisting with the design of prompts, structured output templates, and experimental conditions.
3) Python-based implementation and data collection: The student will help build and run a Python-based experimental pipeline for querying selected LLMs through APIs, storing model outputs, and organizing responses in structured formats such as CSV or JSON.
4) Data cleaning and analysis: The student will help extract recommendations and justifications from LLM outputs, compute evaluation metrics such as decision error, regret, framing sensitivity, and advice invariance, and prepare tables, figures, and summary statistics.
5) Multi-agent protocol testing: The student may assist in implementing and comparing simple multi-agent LLM protocols, such as independent second opinions, structured critique-and-revision, and aggregation of recommendations.
6) Reporting and communication: The student will help document the research pipeline, summarize results, prepare visualizations, and contribute to a final project report and potential academic outputs.
Through this role, the student will gain hands-on experience with LLM APIs, experimental evaluation, Python-based research workflows, prompt design, data analysis, multi-agent AI systems, and responsible AI governance.

Skills required:
Students should have a background in computer science, data science, artificial intelligence, machine learning, engineering, or a related quantitative field. Strong Python programming skills are required, including comfort with APIs, data handling, and basic analysis libraries such as pandas or NumPy. Interest in large language models, natural language processing, machine learning evaluation, human-AI interaction, or responsible AI is strongly preferred. Experience with LLM APIs, prompt design, Git/GitHub, JSON/CSV files, statistical analysis, or data visualization would be an asset. The student should be able to read academic papers, implement reproducible experiments, clean and analyze model outputs, and communicate findings clearly.

Research area: My research explores how people interact with intelligent technologies. I design and evaluate interactive systems that combine AI, visualization, computer vision, and extended reality technologies to support learning, decision-making, creativity, and collaboration. Students in my lab work on projects involving human-AI interaction, educational technologies, mixed-reality experiences, and novel user interfaces, with opportunities to build and evaluate real-world systems.

Student roles:
The students will:

- Develop software components for a mixed-reality assistance system.
- Implement and test interactive guidance features.
- Contribute to the design of user interfaces and user experiences.
- Assist with integrating AR/VR technologies and AI-driven functionality.
- Testing and refining the system through iterative prototyping.
- Participate in prototype evaluation and data collection.
- Analyze results and contribute to research reports and presentations.
- Work closely with graduate students and faculty researchers in a collaborative research environment.
- Contribute to project documentation, reports, and research dissemination activities.

The student will work closely with faculty and graduate student mentors and will gain hands-on experience with AI, computer vision, mixed reality, user experience design, and human-centred technology development. The project emphasizes creativity, experimentation, and the design of technologies that support learning through physical interaction.

Skills required:
Applicants should have completed coursework in Computer Science, Software Engineering, Artificial Intelligence, Human-Computer Interaction, Computer Engineering, or a related field. Experience with programming (e.g., Python, JavaScript, C#, Java, or C++) is required. Familiarity with computer vision, machine learning, AR/VR development, wearable technologies, or user interface design is an asset but not required. Students should enjoy building interactive systems, experimenting with new technologies, and working collaboratively in a research environment.

Research area: Traffic and Transportation Engineering, Analysis, modeling and optimization of the transportation system (Mobility-As-A-Service, public transport, ride-sharing, and emerging transportation technologies), travel behavior and transport economics, data mining and decision analytics (statistical model, machine learning/deep learning), mechanism design and game theory.

Student roles:
Students will participate in the following research activities:
- Literature review in transport clean energy strategic planning, electrical vehicle initiatives, technologies and applications.
- Case study research for cities in Canada including data analysis, technical program summaries, and strategy design
- Technical report and manuscript editing and formatting, assistance in conference papers and technical reports
- Support in programming and develop artificial intelligent models for electric vehicle travels and emission estimations based on multi-dimensional traffic data
- Visualize and integrates data that communicates clear information and conclusions with Python.

Skills required:
- Strong interests in traffic and transportation engineering, sustainable energy systems and artificial intelligence.
- Background in data analysis, system programming and artificial intelligence models.
- Experience in electric vehicles and emission predictions.
- Background in optimization and operation management
- Skilled in python or/and MATLAB software

Research area: My laboratory specializes in advanced cardiovascular blood flow using magnetic resonance imaging. My team develops tools for acquisition, analysis, clinical translation, and innovation of cardiovascular blood flow assessment.

Student roles:
Develop dicom reader intefaces, data loading, navigation, data management, user interaction, and live stream multi-user experience.

Skills required:
For this project, students require expertise in programming and visualization. Python, ML, past experience with VR developments.

Research area: My team specializes in Transportation Engineering, Transportation and Climate Change, Travel Behavior Analysis, Transportation Network Analysis, Machine Learning Methods, Decision-Making Theories, Econometrics, Statistical Modelling, and Big Data Analysis. Advanced decision-making theory and machine learning methods are applied in our research, and we have handled different types of data, such as survey data, RP&SP data, GPS data, longitudinal panel data, etc. Our research field covers different areas, especially in urban planning, environmental studies, smart cities, and intelligent transport systems initiatives.

Student roles:
The intern will be in charge of their project, with a weekly meeting with the supervisor and day-to-day interactions with graduate students. Specific training schemes will be made for every intern based on their goals, unique situation, and capabilities, and adapted regularly. Moreover, I will hold group meetings once per month to let the interns follow up on their teammates’ research and benefit from the expertise available in the lab. The intern will be involved with case studies and the application of basic models to get a full understanding of transportation engineering. More specifically, the intern will participate in the literature review, survey and experiment design, data analysis, model generation, and policy suggestions. We expect the technical report to be of a good level, and to serve as the basis of a scientific paper on the outcome of the project. All the interns in the program can enjoy equal training and development opportunities.

Skills required:
We are looking for students doing a degree in civil engineering, computer engineering, computer science, statistics, psychology, social science, or a related specialty. A good background in modeling (e.g., optimization model, statistical model, etc) and programming (e.g., Python, Matlab, R, etc.) is essential. Some knowledge and interest in machine learning and artificial intelligence are assets. Strong professional software skills (e.g., GIS, simulation software, statistical software, etc.) are a plus. Aptitude for technical writing in English is also important, as we expect the student to write technical reports and possibly scientific papers.

Research area: My research focuses on data structures and algorithms, with a specific emphasis on strings and combinatorics on words, with applications in bioinformatics and information retrieval.

Student roles:
The prospective undergraduate student will play an active role in both the theoretical and computational aspects of the project. They will study relevant literature on string algorithms and combinatorics on words, with a particular focus on repetitions and related concepts such as borders, periods, and runs. Using this foundation, the student will contribute to developing characterizations of closed strings in terms of existing notions of repetition and assist in formulating and proving combinatorial properties. On the algorithmic side, the student will implement and test efficient algorithms in Python or C++ to compute and enumerate closed substrings, analyze their performance, and validate theoretical results through experimentation.

Skills required:
Excellent background in discrete mathematics; Excellent programming skills in Python and C++, Excellent data structure and algorithms and complexity knowledge

Research area: My research focuses on Human Computer Interaction, especially as applied to creative and expressive interaction with digital tools. This includes new interfaces and support tools for performative interaction with computational audio, graphics, and other media; realtime continuous interaction supported by various types of sensing; playfull/ludic interaction; and ambient information displays.

Student roles:
The student(s) will assist in running human-participant studies in our motion-capture laboratory, collection and analysis of study data, and preparation of results for publication. Depending on contribution to the final document, the student(s) could also be listed as coauthor in the final publication.

Skills required:
The 3D crossing system is implemented in the Unity game engine, so experience with Unity is an advantage—otherwise any experience with alternate game engines (e.g. Unreal, Godot) and/or virtual reality development is advantageous.

Research area: Our research group, led by Prof. Hans-Arno Jacobsen, focuses on distributed systems and interdisciplinary quantum computing. The group works on a broad range of quantum computing topics, including quantum algorithms, quantum error correction, quantum software, and quantum machine learning. Prof. Jacobsen also leads Canada’s Quantum Software Consortium (QSC), which explores distributed quantum solutions. This project aligns closely with QSC’s objectives and fits well within the group’s expertise in quantum software and distributed quantum computing.

Student roles:
At the start of the program, the student will review literature on quantum machine learning, quantum encoders, entanglement measures, barren plateaus, tensor-network simulability, and distributed quantum computing. They will also complete tutorials and become familiar with the group’s software environment, coding practices, and research workflow.

The student will first become familiar with analytical tools for studying entanglement in quantum circuits and encoders. This includes reviewing when entanglement can be characterized exactly, for example through Schmidt rank, entanglement entropy, operator entanglement, or other relevant measures, and identifying the limitations of purely analytical approaches for realistic, data-dependent, noisy, or distributed circuits.

The student will then implement and benchmark quantum data-analysis models with different entanglement structures, such as product-state encodings, locally entangling circuits, globally entangling circuits, and task-informed entanglement patterns. Using Python and quantum-computing software such as Qiskit or PennyLane, the student will run numerical simulations to compare model performance, circuit depth, sampling cost, and distribution overhead. Where relevant, they will estimate resources such as the number of inter-QPU cuts, quasiprobability sampling overhead, Bell-pair requirements, or tensor-network bond dimensions.

Throughout the project, the student will maintain a clean codebase using Git, perform computational resource profiling, run experiments on HPC infrastructure, document experiments, organize results, and present weekly progress updates. They will contribute to data analysis, figure preparation, and manuscript writing from the beginning of the internship. By the end of the project, the expected deliverables are a literature summary, a documented benchmarking codebase, reproducible experimental results comparing entanglement regimes, and, provided that sufficient progress and results are obtained, a manuscript released as a public preprint, with the longer-term goal of supporting a future peer-reviewed publication.

Skills required:
The student should be comfortable programming in Python and using common scientific libraries such as NumPy, pandas, and Matplotlib. A solid background in linear algebra, probability, or machine learning is also expected. Familiarity with basic Bash usage and best practices for Git-based version control would be an asset. Prior experience with quantum computing concepts or frameworks such as IBM Qiskit or Xanadu PennyLane is highly desirable but not required. The successful candidate should be motivated to read scientific literature, write clear technical reports, present results, manage time effectively, and work collaboratively in a research environment.

Research area: We ar eworking on creating precision models of hospital service data to provide personalization intervention and treatment for neurodivergent complex pediatric populations.

Student roles:
Work to build machine learning models and be able to talk and relate to clinicians and health scientists.

Skills required:
Applied mathematics, machine learning and neural networks

Research area: Referring to the direction of the United States Federal Trade Commission Children’s Online Privacy Protection Act and the European Union Data Protection Directive, this research adopts the definition of a child as an individual under the age of 13 years old. The first assumption is that children do not understand the concept of privacy. Second assumption is that children will disclose as much information to people they can trust. Many studies found that anthropomorphic toys serve a purpose, as children trusted such designs and felt at ease disclosing private information.

Student roles:
The required role of the student would be tasked with pulling together materials for the literature review on these topics: smart toys and privacy.

Skills required:
The students should have a background in Business, Social Science or Computer Science/Information Technology.

Research area: Natural language understanding (NLU) is a core area of natural language processing (NLP) and involves linguistics-informed classification tasks designed to test whether the LMs possess linguistic competence. This is in contrast to NLG, whose evaluation paradigms may test for fluency, or knowledge benchmarks, which test for knowledge retrieval, like MMLU (Hendrycks et al. 2020). NLU benchmarks include general language understanding benchmarks like GLUE (Wang et al. 2018).

Student roles:
The student will learn to perform one or more the following: linguistic annotation, experiment implementation, result analysis, and scientific writing.
Linguistic annotation comprises of the following:
- Understand the goals and applications of data collection
- Establish annotation guidelines
- Annotate dataset
- Validate and strengthen annotation guidelines if necessary
Experiment implementation comprises of the following:
- Search and compile relevant literature in low-resource NLP, low-resource MT, representation learning, cross-task transfer
- Select a suite of experiments that reflect prior work in the area and our research questions
- Contribute to the codebase of the pipeline using development best practices
- Review pull requests and write documentation
Result analysis comprises of the following:
- Perform statistical analysis on results
- Formalize how the results support or contradict the hypotheses, how they answer our research questions
Scientific writing comprises of the following:
- Motivate the research questions and hypotheses based on current research literature in representation learning, cross-task transfer, and pre-training dynamics
- Review related literature, including and not limited to similar work in other languages, prior work on which this work stands, and works that make similar yet distinct contributions
- Report experimental setup, result analysis in reproducible detail while motivating each major decisions
- Highlight why these contributions are important, and discuss an explanation of the results

Skills required:
The ideal student will have interest and prior experience in programming in Python and version control, as well as one or more of the following stacks:
- NLP stack: neural frameworks like torch and transformers, fine-tuning, evaluation frameworks
- API and inference stack: OpenAI API, vllm, high-performance cloud computing (gcp, slurm, etc)
- Language stack: native proficiency in Cantonese or other Sinitic languages other than Mandarin

Research area: Nadia Tahiri has been an assistant professor in the Department of Computer Science at the Université de Sherbrooke since July 2021. Her research program focuses on the development of new criteria to better understand the processes that generate and maintain biodiversity, mainly through the identification of evolutionary processes. She has worked on various projects, including the development of a new rapid method for detecting and validating horizontal gene transfer events using phylogenetic trees, the design of a phylogeographic approach to understanding wild coffee species in Madagascar, and the construction of explicit hybridization networks using the maximum likelihood approach.

Student roles:
During this internship, students will undertake the task of developing a novel approach for classifying phylogenetic trees, drawing inspiration from immunological studies. This approach will introduce a weighting system that takes into account various factors, such as the abundance of clones within cells, multifurcation, and internodes with a degree of 2. By incorporating these considerations, this innovative approach aims to identify carcinogenic lineages by detecting an excessive presence of specific clones in comparison to others within the same cell population.

Furthermore, students will be expected to propose a new metric for evaluating phylogenetic trees, choosing from options such as Robinson and Foulds distance, Quartet distance, or least-squares distance. Alongside this, interns will be responsible for implementing an efficient data structure that facilitates seamless insertion, deletion, and search operations. Subsequently, they will integrate these metrics into the existing algorithms of the laboratory, specifically those developed by Tahiri et al. in 2018 and 2022, in collaboration with the research teams from the University of Sherbrooke and the University of Montpellier labs.

Finally, as part of their internship, students will have the opportunity to analyze real-world data alongside the aforementioned research teams.

Skills required:
Good programming skills (e.g. C, C++, Python).
Algorithm. Graph Theory. Some knowledge in
bioinformatics (a plus). The student must be very
autonomous and show initiative. The student must
speak and write in English or French.

Research area: We are a team with various expertise working on pain neurophysiology and the brain and we conduct research in the broad field of Neuroscience with various approaches and methodology. We aim to understand the mechanisms of pain perception and regulation and how we can develop innovative non-pharmacological approaches for pain management.

Student roles:
Ability to communicate well and work with a team of students of different backgrounds (from computer science to health care), different origins and languages, as well as different academic levels (undergraduate to postdoc).

Skills required:
The student should have basic skills to work with Python or Maltlab or be interested in learning these software. As far as motivation is demonstrated, the candidate will be considered. Of course, if the candidate is motivated and already skilled with these software, it is a competitive advantage.

Research area: Nadia Tahiri received her M.Sc. and Ph.D. in Computer Science from the University of Quebec in Montreal, Canada. She was a postdoctoral researcher and worked on QSAR/PBPK (machine learning) model prediction in environmental health sciences at Université de Montréal, Canada. She has received several awards and grants during her career. She is currently an Assistant Professor in the Department of Computer Science at the University of Sherbrooke, Canada. Her research interests include bioinformatics, phylogenetic tree, phylogeography, clustering, classification, computational biology, supertree, consensus tree, and machine learning theory.

Student roles:
In this internship, students will develop a new approach to classifying phylogenetic trees (based on immunological studies) that will allow giving weight to the abundance of clones present in cells. This innovative approach will make it possible to highlight carcinogenic lineages, by an overabundance of a given clone compared to other clones present in the same cell. In addition, students will propose a new phylogenetic tree metric (e.g. Robinson and Foulds distance, Quartet distance or least-squares distance will be chosen). Interns will implement an efficient data structure with respect to inserting, deleting, and searching for data. They will then integrate the metrics into the lab's algorithms (Tahiri et al. 2018 and Tahiri et al. 2022) with the collaboration of the University of Sherbrooke and the University of Montpellier labs. Finally, they will analyze real data with the same research teams.

Skills required:
Good programming skills (e.g. C, C++, Python). Algorithm. Graph Theory. Some knowledge in bioinformatics (a plus). The student must be very autonomous and show initiative. The student must speak and write in English or French.

Research area: Signal & Image Processing Computer Vision Artificial Intelligence

Student roles:
- State of the art in unsupervised and self-supervised video representation learning
- Identify and analyze relevant clustering methods and deep learning architectures
- Collect, download, and preprocess benchmark video datasets (UCF-101, Kinetics)
- Implement data loading and feature extraction pipelines
- Implement and adapt deep learning architectures (CNNs, Transformers, pre-trained encoders) for video embedding generation
- Integrate and configure clustering algorithms (k-means, hierarchical)
- Explore dimensionality reduction techniques (e.g., t-SNE) for visualization and preprocessing
- Evaluate clustering quality using internal and external metrics (silhouette score, Davies-Bouldin)
- Build a modular, reproducible, and well-documented codebase
- Write regular progress reports for the supervisor
- Prepare a final report, present results through meetings

Skills required:
- Linear algebra
- Probability and statistics
- Optimization theory
- Supervised and unsupervised learning fundamentals
- Deep learning architectures: CNNs, RNNs, LSTMs, Transformers
- Deep learning frameworks: PyTorch or Tensorflow
- Libraries: OpenCV, Scikit-Learn, NumPy, Matplotlib
- Video processing tools: FFMpeg, Decord
- Self-supervised and contrastive learning methods
- Clustering algorithms
- Pre-trained models and transfer learning
- Image and video feature extraction
- Familiarity with video datasets (UCF101, Kinetics, HMDB51)
- Knowledge of clustering evaluation metrics (silhouette score, Davies-Bouldin)

Research area: In recent years, I have become interested in the use of immersive technologies to support the rehabilitation of people living with schizophrenia in prison and forensic psychiatry. My current research focuses more specifically on the contribution that different forms of immersive technology can make to relaxation, social skills training and emotion regulation.

Student roles:
Depending on the student’s background and level of fluency in French, they will play a role in either 1) conducting codesign workshops with patients and court members to help define and design the virtual environment, or 2) programming the virtual environment, characters, and animations. If the student participates in the codesign workshops, they will play a role in collecting and analyzing qualitative data. The student will also take part in team meetings held throughout the project. They will also be involved in drafting written or oral communications related to the project. In all cases, the student will have the opportunity and necessary autonomy to familiarize themselves with the research topic, the internship environment, the required activities, and the technologies used (virtual reality applications, softwares, qualitative analysis tools, etc.).

Skills required:
We are looking for a student with a background in design or in computer science and programming. For design students, skills in graphic design, UX design, or participatory design are a valuable asset. For computer science and programming students, an understanding of object-oriented programming is a valuable asset. Knowledge of Unity or Unreal for game development is an additional asset. The student should be comfortable interacting with people with mental health disorders. A basic knowledge of French is an asset in this project.

Research area: I do research in software engineering. I work on various techniques that involve source code analysis, execution traces, code transformation, software dependencies, build systems, with applications to internet-based systems and generative art systems. I have worked in France, Sweden and Canada with graduate students from all five continents. More details about my contributions and our current research crew: https://softwarediversity.eu/

Student roles:
The student will perform the following tasks
- curate a list of social media platforms that can be queries automatically and where artists share code
- build a diverse dataset of posts where artists share code and art
- establish a typology of different sharing practices
- report on this work in scientific report

Skills required:
Data mining, data analysis, image analysis, code analysis, an interest for digital art and internet culture

Research area: My research area is security and privacy, as well as education/teaching, with some application of Machine Learning techniques. In fact, my research now combines these things together. Given that cybersecurity skills are in high demand (i.e. there are considerably more cybersecurity jobs than qualified candidates for those jobs) I am interested in how to educate people more effectively and more efficiently in the security/privacy area and the effects of GenAI on education. Besides doing technical research in the authentication area, I also do research in the Scholarship of Teaching and Learning (SOTL) area too.

Student roles:
The student will be developing game features, based on sound pedagogical or cognitive psychological learning principles. The student will be testing these software features with real users and revising the code appropriately, with an overall objective of optimizing long-term retention of cybersecurity skills, knowledge and ability for users.

Specifically, the following will be required:
. Implement and test different features for a collaborative learning game with users
. Capture defects and improvements using github and github issues
. Prioritize and then incorporate required fixes and feature improvements, in consultation with other team members
. Collect Data from a "reasonable" set of users and analyze that data, critically, for ways to optimize learning further for a wide range of users
. Timelines need to be defined in collaboration with the student, based on their specific and real experience, but (roughly):
- 2 weeks of system and game understanding and code familiarization
- 2 weeks of implementation and feature testing
- 2 weeks of setup and then testing with real users and the resulting related bug fixes and feature enhancements
- a further 2 weeks of testing with a distinct set of real users to collect actual data for analysis
- 2 weeks of analysis of that collected data and the creation of a technical report draft
- review of that report with graduate student and supervisor
- 2 weeks of revisions of the report, with extra time required to collect more data or re-collect previous data, depending on the analysis results.
- 2 weeks of slop time, if above estimates are overly optimistic

Basic deliverables: back-end python design work, front-end UI/UX testing and revision, use of AI/ML methods (e.g. chatgpt or Claude) to optimize learning, "other" innovative ways of improving user learning and retention (e.g. story-telling or multimedia synthesis)

Skills required:
The ideal student would have computer programming aptitude (e.g. python) and the ability to work with others in a programming environment. Industrial programming experience is, therefore, an asset, although course-based programming skills are fine too. The ability to work independently and use tools like github, effectively, would be assets, as would an appreciation of the importance of writing maintainable code. Additionally, the candidate would have an interest in user-based design and applied Artificial Intelligence, user testing, or an interest in education and learning, since the basic purpose of the research is to improve education using online computer-based tools and methods.

Research area: Creating control software, sensors and actuators for robotic systems with a focus on collaborative and autonomous robots. Mentoring students is a very important part of my job.

Student roles:
The student is expected to design, program and experimentally test software for controlling the robot arm. They will be working as a member of the robotics research team at McMaster University.

Skills required:
Programming in C++.
Some knowledge of robotics.

Research area: Choice modeling is concerned with representing the preferences of consumers (for example, product preferences retail, mode preference in transportation, etc.) as a mathematical model. Those models can be learned (calibrated) by historical data, and then used to predict future consumer choices. Choice models are useful to gain market insights, but are particularly suited to be used for further optimization (for example, to optimize a new assortment that maximizes the total profit).

Student roles:
The student will carry out a well-defined research project. While the student will be supervised, she/he is encouraged to work as independently as possible and propose solutions to the given problem. Research will be carried out either at CRI2GS at ESG UQAM, or at the Interuniversity Research Centre for Enterprise Networks, Logistics and Transportation (CIRRELT). Ideally, the student will contribute to writing a scientific paper that is submitted to a scientific journal.

Skills required:
Good programming and Mathematics skills are essential. Knowledge in mathematical optimization (linear programming, integer programming, etc.) and/or machine learning or statistical learning are a plus.

Research area: My research area is in the area of advanced wireless communications, mobile networking, machine learning, and multi-agent systems. Information about our research publications can be found at: https://home.cc.umanitoba.ca/~hossaina

Student roles:
The student will work with a PhD student and a Post-Doctoral Fellow in my research group, and help them in developing simulation modules and collection of simulation results.

Skills required:
The student will need to have background in communications and control theory, programming experience in MATLAB and/or Python. Background in optimization theory and machine learning techniques will be a plus.

Research area: My research focuses on Sustainable Artificial Intelligence (Sustainable AI), with an emphasis on developing efficient, scalable, and environmentally responsible AI systems. As modern AI models continue to grow in size and computational requirements, my work explores methods to reduce their energy consumption, carbon footprint, and infrastructure costs while maintaining high performance and reliability. In addition, I am interested in Large Language Model (LLM) optimization and efficiency, particularly in reducing model complexity and designing lightweight architectures that can be deployed in resource-constrained environments.

Student roles:
The student will:

- Read and summarize relevant research papers.
- Reproduce existing token pruning methods from the literature.
- Implement experimental pipelines in Python and PyTorch.
- Run inference experiments on selected LLMs.
- Collect energy, latency, memory, and quality metrics.
- Analyze results using statistical and visualization tools.
- Prepare weekly progress reports.
- Present findings during research meetings.
- Contribute to writing research papers and technical reports.

Skills required:
The ideal student should have:

- Advanced programming experience in Python.
- Fundamental knowledge of machine learning and deep learning concepts.
- Familiarity with neural networks and transformer architectures.
- Basic understanding of transformer-based LLMs
- Experience using PyTorch or a willingness to learn it.
- Ability to read and understand scientific research papers.
- Basic knowledge of Linux and command-line tools.
- Familiarity with data analysis and visualization tools such as Pandas and Matplotlib.
- Strong analytical and problem-solving skills.
- Interest in sustainable AI, efficient computing, and systems optimization.

Research area: Ce domaine de recherche porte sur l’intelligence artificielle multimodale et les techniques de fusion intelligente de données hétérogènes. Nos travaux visent à développer des modèles capables d’intégrer efficacement plusieurs sources d’information, telles que les images, les vidéos, les images thermiques, les données tactiles, l’audio et les signaux physiologiques, tout en réduisant le coût computationnel afin de faciliter leur déploiement dans des environnements réels. Ces approches ont des applications importantes en santé, notamment pour soutenir le dépistage de problèmes neurodéveloppementaux, ainsi qu’en perception artificielle, en modélisation intelligente et en systèmes d’aide à la décision.

Student roles:
Dans le cadre de ce stage de 12 semaines, les stagiaires participeront aux différentes étapes d’un projet portant sur le développement d’approches neuromorphiques et impulsionnelles pour la compréhension multimodale de scènes. Le projet s’appuie sur des travaux déjà amorcés depuis plusieurs mois par deux étudiants à la maîtrise, notamment en collecte de données, en développement de modèles et en exploration d’approches d’apprentissage profond. Les stagiaires collaboreront avec ces étudiants afin de contribuer à l’avancement du projet et de tirer parti des résultats déjà obtenus.
Leur rôle comprendra d’abord une revue de la littérature portant sur les réseaux de neurones impulsionnels, la fusion de données multimodales, la perception artificielle et l’apprentissage profond écoénergétique. Ils contribueront ensuite à l’examen des données déjà collectées dans nos laboratoires, à leur organisation, à leur préparation et, au besoin, à de nouvelles collectes de données multimodales. Ils participeront également à l’élaboration d’une stratégie rigoureuse d’acquisition, de structuration et de gestion des données afin d’assurer la qualité, la cohérence et la reproductibilité des expérimentations.
Les stagiaires contribueront au prétraitement des données, ainsi qu’à l’entraînement, au test et à l’évaluation de modèles classiques d’apprentissage profond et d’architectures neuromorphiques ou impulsionnelles. Les résultats seront comparés selon plusieurs critères, notamment la précision, la complexité du modèle, le coût computationnel, le temps d’inférence, la consommation énergétique estimée et le potentiel de déploiement sur des dispositifs embarqués.
Enfin, les stagiaires participeront à l’organisation du code, à la documentation technique, à l’analyse des résultats et à la préparation de figures, de tableaux, de rapports ou de résumés scientifiques. Selon l’avancement du projet, ils pourront également contribuer à la préparation de publications scientifiques.

Skills required:
La candidate ou le candidat devrait avoir une formation en informatique, en génie informatique, en intelligence artificielle, en science des données ou dans un domaine connexe. Des connaissances de base en apprentissage automatique, en apprentissage profond et en programmation Python sont souhaitées. Une expérience avec PyTorch, TensorFlow, la vision par ordinateur, le traitement du signal ou les données multimodales serait très importante. La personne devrait être motivée, autonome et capable de lire des articles scientifiques en anglais. Une expérience en recherche appliquée, en approches neuromorphiques ou avec des bibliothèques telles que Norse et snnTorch constituerait un grand atout.

Research area: The research focuses on construction project portfolio management, with an emphasis on risk analysis and computational decision-support systems. It aims to develop schedule-risk-financial models and software tools for evaluating risk diversification in construction and infrastructure project portfolios. The research integrates project schedules, risk drivers, inter-project dependencies, cost data, and financial performance metrics to support strategic portfolio decisions, budget allocation, and portfolio optimization. The ultimate objective is to provide project owners and decision-makers with scalable analytical tools for improving portfolio resilience and financial performance under uncertainty.

Student roles:
The student will support the development of a research software prototype for a schedule-risk-financial model in construction project portfolio management. The role is suitable for a student with strong programming skills who is interested in applying software development to project analytics, risk modeling, and decision-support systems.
The student’s main responsibility will be to implement the software structure in Python using object-oriented programming. This includes developing modular and reusable code, organizing input and output data formats, processing project-related datasets, and supporting the integration of schedule, risk, and financial information within the prototype.
The student will also assist with building the simulation workflow, testing model functions, debugging code, documenting the software, and preparing structured outputs such as tables, figures, and scenario comparison results. The work will involve close collaboration with the research team to translate the proposed conceptual model into a functional computational tool.

Skills required:
The student should have a strong background in Python programming and software development. Experience with object-oriented programming, data structures, data processing, and modular code design is required.
Knowledge of project management, construction scheduling, risk analysis and financial modeling would be an asset. The student should be able to work with structured and semi-structured data, translate conceptual model requirements into code, and contribute to the development of a reliable research software prototype.

Research area: Our group designs, synthesizes, and applies unique fluorescent molecules. We use computational chemistry to guide synthesis and identify fluorophores optimized for super-resolution microscopy.

Student roles:
The student will work on-site to receive mentorship and participate in group meetings, presentations, and informal discussions that support professional growth. They will receive foundational training in computational chemistry, including command-line navigation, input file preparation, and the execution of quantum chemical calculations using Gaussian and ORCA. They will analyze ground and excited state optimizations, as well as predict absorbance and fluorescence spectra of many different fluorophores modified with electron-donating and electron-withdrawing groups.

By the end of the internship, the student will have developed technical skills in computational chemistry, data analysis, and scientific documentation. These skills are highly transferable to careers in academia, industry, and government. They will also gain broader research experience, including problem-solving, critical thinking, and contributing to interdisciplinary discussions on fluorophore design.

Skills required:
The student should have a classroom understanding of organic chemistry and be comfortable using Microsoft Excel and Word. Familiarity with command-line tools, Gaussian, Python, or Bash is not required, but exposure to any of these areas would be helpful. Most importantly, the student should be enthusiastic about learning and able to work respectfully and collaboratively within a diverse research group.

Research area: My research is generally in the area of scientific computing. My research interests include numerical solutions of partial differential equations, fast linear and nonlinear solvers, with applications in computational finance, computer graphics, medical image processing, and parallel computation on high performance platforms.

Student roles:
Students will be involved in:

1) studying of machine learning techniques

2) studying of numerical methods for computational finance

3) implementation of the computational methods

4) writing up of the results including the documentation of the code developed

Skills required:
Students working in this project are preferred to have the following background:

1. Calculus and linear algebra
2. Numerical computation
3. Programming in python
(Background in finance/machine learning is not required, but it would be useful)

Research area: Dr. Gueziri's research focuses on the design and evaluation of ultrasound-guided navigation systems in neurosurgery, the development of new smart sensor technologies, and the application of immersive visualization in medicine. This work encompasses three key research areas: surgical assistance, education and simulation, and the design of low-cost medical systems.

Student roles:
The student will contribute to the development and evaluation of image processing methods for ultrasound-based surgical navigation in spine surgery. Their main role will be to design, implement, and test algorithms for processing intraoperative ultrasound images and supporting accurate registration between ultrasound data and 3D anatomical models.

The student will work on tasks such as image enhancement, feature extraction, segmentation, and preparation of ultrasound data for registration. They will also contribute to the implementation and evaluation of registration methods, with a particular focus on measuring accuracy, robustness, and repeatability under realistic experimental conditions.

A key part of the project will involve quantitative validation using ex vivo spine models. The student will help design evaluation protocols, process experimental datasets, compute registration error metrics, and analyze the factors that influence registration performance. This may include assessing target registration error, surface alignment accuracy, sensitivity to initialization, and variability across repeated acquisitions.

The student will be expected to document their methods, maintain clean and reusable code, and communicate results clearly through reports, figures, and presentations. They will work closely with the research team in an interdisciplinary environment combining computer vision, medical imaging, and surgical navigation.

Through this project, the student will gain hands-on experience in medical image processing, image registration, quantitative validation, and the development of practical tools for image-guided surgery.

Skills required:
- Background in computer science, biomedical engineering, medical imaging, electrical engineering, or a related field
- Strong programming skills in Python and/or C++
- Experience in computer vision, image processing, machine learning, or medical image analysis
- Experience with 3D data, image registration, segmentation, ultrasound imaging, or surgical navigation is highly desirable
- Ability to work in an interdisciplinary research environment involving engineering, computer science, and clinical applications
- Interest in developing practical solutions and validating algorithms on realistic ex vivo models
- Strong problem-solving skills, autonomy, scientific curiosity, and clear communication skills

Research area: My research includes explorations of the social implications of advanced technolgies such as artificial intelligence and mixed realities/VR. I am a computer scientist engaged in developing prototypes, research, and research-creation that directs and critiques technologies through novel approaches and collaborations with communities.

Student roles:
There are a host of new machine-learning object-tracking/computer-vision tools on the market,
both open-source and comemrcial, and the student will evaluate, select, and develop a reusable
approach as part of the larger research-creation project. The student will work under the
supervision of the faculty-member, but must be comfortable finding options, code-documentation,
recommendations and code-samples online. They must also be comfortable with open-ended exploration, building prototypes of physical and digital ideas.

Skills required:
A computer-science student with either experience in computer-vision, or comfort learning new
APIs and tools to develop the object-tracking portion of the project. They must be a comfortable
coder able to develop well-documented, modular code as part of a larger project
OR
An engineering or digital-art student comfortable with Arduino, physical prototyping, electronics prototyping
OR
An industrial design or other student comfortable with physical prototyping, installation art, fabrication

Research area: Convex analysis provides powerful tools to solve optimization problems in a wide range of fields: business (minimizing costs, maximizing profit), economics, etc. As an extension of the classical linear programming theory, convex optimization algorithms are among the most used algorithms ever. While operators routinely involved in convex analysis have been used theoretically for decades, very limited symbolic computation of convex operators is available today and manual calculation is still the only method to build examples. Computational Convex Analysis is my specialty and I focus on building symbolic-numeric algorithms to compute convex operators in the most efficient way.

Student roles:
The student will first become familiar with the existing numerical library and visualization tools. Then (s)he will implement the data structure to manipulate operators and code the visualization tools in a mathematical software (MATLAB, Python or JavaScript/TypeScript). The student will then test the code on specific numerical examples. The internship will provide exposure and training in software engineering, scientific computing, and visualization. The student is not expected to create new algorithms (although volunteering new ideas is definitely welcome) but to implement fully specified algorithms in a high level programming language. Exploration of parallelization of the computation may be investigated. The background required is experience in programming in a high level programming language (C/C++, Java, python, MATLAB, etc.), and interest in Mathematics. Any background in optimization or AI is welcome but not required.

Skills required:
An interest in mathematics, good programming skills, and good oral communication skills are all that is required. Most of the development will be performed in MATLAB, Python or JavaScript/TypeScript. Some knowledge of software engineering would be helpful, especially unit/coverage/acceptance testing but can be taught at the beginning of the project. Mathematical concepts and ideas in convex analysis, complexity, and numerical analysis are elementary and at the level of a third year undergraduate student. Experience with AI and LLMs-especially for coding-is optional, and would be leveraged if available.

Research area: My area is continuous optimization. In particular, I focus on optimization algorithms, including their design, convergence/complexity analysis and efficient implementation, for solving problems arising in machine learning and data science. My research uses theoretical tools from convex analysis, monotone operator theory, applied probability and theoretical computer science. Moreover, my interests also concern efficient implementation of optimization algorithms in modern large scale computing platforms.

Student roles:
The student will be responsible for reading research papers, which will be mathematical and computational, that contain the algorithms that we will study and the performance estimation problem (PEP) framework which is the main tool we will use for computer-assisted analyses of the algorithms. The student will experiment with this platform and then use it to investigate the convergence analyses of algorithms in question. This will involve many meetings between the PI and the
student for trouble-shooting and debugging as well as studying algorithms and analyses from papers. During this process, there will be regular meetings between the PI and the student. The student will be a part of the research group of PI which involves multiple graduate students, with potential collaboration opportunities. The student will show their progress to the PI and to the group via oral presentations and written reports.

Skills required:
The student is required to have mathematical maturity to be able to read and understand highly mathematical papers that develop optimization algorithms and their convergence analysis. Particularly, the PI expects proficiency with optimization theory, machine learning, linear algebra, probability and real analysis. The the project has major computational aspects as well: as a result, the student is expected to be proficient with programming languages and environments including Julia, Python, C/C++, PyTorch.

Research area: The laboratory area of expertise covers design and evaluation of interactive and intelligent assistive systems using multi-modality including haptic as communication channel and artificial intelligence. These systems are usually portable, but also concern design of semi-autonomous robotic mechanisms such as cable driven parallel robots. The main objective is to design, implement and evaluate technologies for people with disability or in loss of autonomy.

Student roles:
The role of the student is to develop on an augmented reality glasses (Hololens) a human avatar able to interact with a human in an industrial installation including collaborative robots. The design will be done with ROS, JuliaRobotics or RoboDK.

Skills required:
The student must master 3D design and visual rendering techniques. Preferably, the ideal candidate has the knowledge on rotation matrices, generative artificial intelligence. Finally, knowledge of robotics, in particular manipulators is desirable. An additional asset would be to have knowledge of haptics and virtual reality.

Research area: B+-trees are fundamental data structures that are widely used in database management systems, file systems, and key-value storage engines. Efficient concurrent B+-tree implementations are essential for supporting high-throughput data access on modern multicore architectures. Although numerous concurrent B+-tree designs have been proposed, including recent latch-free approaches such as FB-tree [2], these implementations often rely on sophisticated synchronization mechanisms, making them difficult to design, implement, verify, and maintain.

Student roles:
1. Literature Review and Analysis: Conducting a comprehensive review of research papers on concurrent B+-trees, Multi-Compare-and-Swap (MCAS), lock-free data structures, and multicore synchronization techniques. The student will summarize key findings, identify research gaps, and develop a strong theoretical foundation for the project.

2. Algorithm Design and Development: Investigating existing concurrent B+-tree designs and developing new algorithms or enhancements based on MCAS. The student will analyze design alternatives and explore techniques to improve implementation simplicity, correctness, and scalability.

3. Software Implementation: Implementing concurrent B+-tree algorithms in C++, with an emphasis on performance, correctness, modularity, and code maintainability. The student will also develop benchmarking and testing frameworks to support experimental evaluation.

4. Experimental Evaluation: Designing and conducting experiments on multicore shared-memory systems to evaluate the scalability, throughput, latency, memory overhead, and correctness of the proposed methods. The student will compare the proposed approach against representative state-of-the-art concurrent B+-tree implementations using both synthetic and realistic workloads.

5. Research Reporting and Scientific Writing: Contributing to the preparation of technical reports and research manuscripts that document the methodology, experimental results, analysis, and conclusions. The student will also create figures, tables, and visualizations to effectively communicate the research findings.

Skills required:
The ideal student should have a strong background in computer science or a related field, with a specific interest in algorithms and data structures. Proficiency in C++ programming is essential, including experience with object-oriented design, algorithm implementation, and efficient memory management.

Research area: My research specializes in software engineering, with a focus on applying AI and data-driven techniques to improve software development processes. I work on mining software repositories, optimizing continuous integration and delivery (CI/CD), and leveraging large language models (LLMs) for tasks like code generation, testing, and configuration migration.

Student roles:
- Review the literature to identify common types of configuration debt
- Collect CI/CD, containerization, and infrastructure configuration files from open-source repositories
- Develop static analysis tools to detect configuration debt patterns
- Measure the prevalence, co-occurrence, and evolution of configuration debt
- Investigate whether configuration debt is explicitly acknowledged by developers
- Study the relationship between configuration debt and project outcomes such as build failures and maintenance effort
- Analyze results and produce figures, tables, and statistical summaries
- Contribute to the technical report and research paper

Skills required:
- Proficiency in Python
- Familiarity with Git and GitHub
- Basic Linux and command-line skills
- Basic data analysis skills (e.g., pandas and statistical analysis)
- Ability to read and understand research papers
- Experience with CI/CD services and their configuration
- Experience with DevOps tools such as Docker, Terraform, or Kubernetes is a plus but not required

Research area: The research focus of my Lab at the University of Montreal is on Neuroscience, Brain Imaging and Artificial Intelligence. The aim of our multidisciplinary research project is to improve our understanding of brain function and dysfunction. Our research relies on analyzing brain signals recorded with multiple techniques including electroencephalography (EEG) and magnetoencephalography (MEG). The data analysis methods used in our lab rely on state-of-the-art methods that come from numerous fields, such as Artificial Intelligence, Biomedical Engineering, Computer Science, Mathematics, Neuro-informatics, Neuro-Engineering, Signal processing, etc. The intersection between Neuroscience, Neuro-imaging and AI is an emerging field with cutting-edge research applications.

Student roles:
Role of the student:
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-Brief literature research to understand the background of the research project
-Explore/learn the data anlysis tools that will be needed for the project
-Discuss project objectives and data analyses options with the supervisor
-Carry out the analysis and provide regular reports
-Participate in Laboratory life (Lab meetings and scientific events)
-Maintain a clean and documented codebase
-Provide a short report of the obtained results at the end of the project and possibly contribute to a research article.

Skills required:
The required skills include any of the following:

Programming (proficient in Python)
Signal processing knowledge (e.g. time and frequency-domain analyses, fractal analyses)
Experience with brain data analysis (e.g. MEG or EEG)
Machine-learning methods (theory and practice, Scikit-Learn and/or Pytorch)
Experience with foundation models (Self-Supervised Learning / Transformers / etc)
Knowledge of (or at least a strong interest in) neuroscience and higher-order cognitive processes, including consciousness and creativity
Prospective students are not expected to have all the above skills, but at least 2-3 skills from the above list is expected.

Research area: Dr. Abdulrazak research interests include Ambient-Intelligence (AmI), IoT, Smart-Homes, Smart-Cities and Digital health. He works on various R&D projects at the AMI laboratory. The projects range from Assistive Home Environment for the aging people, Electronic Organizer, Pervasive Reminder System, Mobility Assistance, Smart cities for active aging, etc. These technologies aim at being used either inside or outside (city, home, retirement communities, community clinics, on the street, etc.)

Student roles:
The selected intern will serve as a core developer and researcher for the next iteration (QueFaire v2) of our assistive framework. The role bridges the gap between software engineering, semantic reasoning, and physical computing to deliver a cohesive user ecosystem.
Key responsibilities and expected deliverables include:
• Algorithmic Text Analysis: Designing and developing an intelligent text-parsing algorithm to scrape, process, and structure unstructured activity descriptions from various social media networks.
• Semantic Reasoning Engine: Architecting an automated reasoning engine that evaluates real-time environmental context against highly individualized user profiles to safely recommend optimal, personalized activities.
• Advanced Interaction Mediums: Engineering intuitive, accessible human-computer interaction modules—spanning mobile apps and embedded ambient devices—tailored explicitly for non-technical older adults.
• System Integration: Deploying and testing the final algorithmic components on localized embedded infrastructure to ensure real-time, low-latency execution.
Through this internship, the student will transition from theoretical algorithms to a tangible, high-impact deployment. This position offers rigorous technical exposure to end-to-end AI pipelines, cross-platform mobile-embedded orchestration, and assistive technology design—skills that are highly sought after in advanced graduate research and cutting-edge industrial R&D labs—while directly improving the quality of life for aging populations.

Skills required:
Core Qualifications:
• Research Motivation: High degree of self-motivation to engage with complex, multi-disciplinary R&D challenges.
• Programming Proficiency: Superior software development skills with core competence in object-oriented programming .
Technical Assets:
• Practical experience in Mobile Application Programming.
• Foundational skills operating within Linux environments and deploying software on embedded hardware.
• Practical understanding of Web Services and cross-platform API integrations.
• Basic curiosity regarding Natural Language Processing (NLP), Semantic Reasoning Engine design, or Human-Computer Interaction (HCI).