Computer Science

Research area: Debajyoti Mondal is an associate professor in the Department of Computer Science at the University of Saskatchewan, Canada. His primary research interest is in visualization algorithms, graph drawing, visual analytics of large networks. His work spans interactive network layouts, cartographic representations, geometric representations, metro maps, etc. A major part of the visualization process is to map the abstract information to some geometric data for rendering, which inspires his research in computational geometry and graph algorithms.

Student roles:
The student will work closely with his advisor. The initial phase of the project is learning intensive, where the student will learn the existing streaming algorithms, tools, and modeling techniques. The student will also independently review papers on adaptive algorithms and visualizations from premier venues. The next phase would be a development phase, where the student will develop and implement algorithms. These algorithms will be integrated into a visualization system. In the final phase, the students will conduct a user evaluation to the evaluate performance of the developed system. The student will regularly meet the advisor to discuss the progress and challenges.

In this project, the student will gain unique skills in developing visualization algorithms and systems. The software library developed by the student will be a great asset for building future visualization software. The student will be able to develop unique skills to visualize large datasets, which will place the student in a favorable position in the job market. Specifically, the student will gain expertise in the areas of machine learning, statistical modeling, large-scale data processing techniques, user interactions, and smart visualization systems.

Being in a research environment, the student will also be familiar with the research contacted by the graduate students of the Visualization Lab (Saskatchewan) and Human-Computer Interaction Lab (Saskatchewan), which may encourage the students to plan a graduate career in this area.

Skills required:
Strong knowledge of theoretical computer science, data structure, algorithms, proof techniques, and discrete mathematics. Good knowledge of computational geometry or graph theory (graph terminologies and properties) would be an asset.

Research area: My primary research specializes in applied machine learning, deep learning, and the modeling of complex, high-dimensional data structures. By leveraging high-performance computing, my research applies advanced computational models to real-world healthcare challenges, particularly in the domains of continuous healthcare monitoring and elderly safety. The overarching goal of my research programs is to bridge theoretical computer science with health informatics, developing intelligent, algorithm-driven systems that can translate complex human behavioral and physiological data into reliable and actionable insights.

Student roles:
1. Write robust Python/R scripts to handle missing values, normalize ordinal scales, and format the data for machine learning ingestion.
2. Responsible for tuning the regularization parameters to ensure the resulting symptom network is sparse and interpretable.
3. Build, train, and test the tree-based machine learning models.
4. Translate mathematical adjacency matrices into clear, publication-ready network visualizations where node size, color, and edge thickness immediately communicate symptom importance.
5. Maintain a clean, well-commented GitHub repository so the research can be reproduced or expanded upon in the future.
6. Draft a manuscript.

Skills required:
1. Major in Computing Science, Data Science, Statistics, 3rd- or 4th-year undergraduate who has completed core upper-level coursework in data structures, statistics, or introductory machine learning.
2. Strong, hands-on experience with Python. Should be comfortable writing clean, modular code and troubleshooting errors independently.
3. Better to have proficiency with pandas and numpy.
4. Has an understanding of basic statistical concepts (variance, correlation) and linear algebra (matrices).
5. Should understand the basic mechanics of training a model, splitting data into train/test sets.
6. Previous experience reading academic papers, reproducing code from GitHub repositories, or working on open-ended technical problems.

Research area: My research focuses on intelligent communication networks, IoT systems, Age of Information, network science, edge computing, 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, cyber-physical systems, vehicular networks, and next-generation communication systems.

Student roles:
The student will be responsible for implementing and evaluating a Python-based graph simulation framework for freshness-aware resource allocation in edge-IoT networks. The student will begin by reviewing basic concepts related to IoT systems, edge computing, graph-based network models, resource allocation, and information freshness.

The student will then build a simplified edge-IoT network model in which nodes represent sensors, edge servers, access points, vehicles, or connected devices. Links will represent possible communication or task-offloading paths. The student will implement several resource-allocation strategies, including random assignment, nearest-server assignment, least-loaded-server assignment, and freshness-aware greedy assignment.

The student will run experiments under different network conditions, such as varying node density, server capacity, communication delay, update-generation rate, and task-arrival rate. They will evaluate the strategies using metrics such as average Age of Information, delay, task success rate, load balance, communication cost, and resource utilization.

The student will create plots and tables to compare the methods, maintain well-documented code, participate in regular research meetings, and prepare a final report and presentation. The role is implementation-focused; the student is not expected to derive new mathematical models or solve advanced optimization problems analytically.

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, graph theory, data analysis, probability, or algorithms is an asset. The student is not expected to know advanced queueing theory, Age of Information analysis, or mathematical optimization. A motivated student with experience in coding, simulation, and visualizing experimental results should be able to complete the project successfully.

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:
* Develop graph-based program representations using Abstract Syntax Trees (AST), Control Flow Graphs (CFG), and Data Flow Graphs (DFG) for software vulnerability analysis.
* Design and implement machine learning and large language model (LLM)-based techniques for multi-label vulnerability detection and CWE classification.
* Conduct experiments, evaluate model performance on benchmark datasets and real-world codebases, and analyze the effectiveness of retrieval-augmented reasoning approaches.
* Assist in preparing research reports, technical documentation, and publications related to automated software vulnerability detection and localization.

Skills required:
* Strong programming skills in Python and familiarity with software development practices and debugging techniques.
* Background knowledge in machine learning, deep learning, or large language models (LLMs) applied to software engineering or cybersecurity problems.
* Understanding of software vulnerability analysis, secure coding principles, or program analysis techniques such as AST, CFG, and DFG.
* Experience with data analysis, experimental evaluation, and the use of AI/ML frameworks such as PyTorch or TensorFlow.
* Strong analytical, problem-solving, and technical writing skills, with the ability to work independently in a research-oriented environment.

Research area: My general research area is Software Engineering. In particular, my main research focuses on improving (AI) software performance and developing fast and efficient software in fast-paced development and release practices (e.g., DevOps). My detailed research interests include software performance engineering, AI software system quality, and web GUI testing.

Student roles:
Environment Setup: Install and configure local LLM runtimes (e.g., Ollama, Llama.cpp) and monitoring tools like NVIDIA-SMI or PowerAPI.
Experimental Execution: Run systematic benchmarks across various model architectures and quantization settings.
Data Aggregation: Collect energy-per-token (EpT) metrics and analyze the correlation between prompt complexity and power spikes.
Visual Analysis: Use Matplotlib or Seaborn to create energy-performance trade-off curves.
Drafting Results: Summarize key findings for a research publication (4-10 pages) focused on sustainable AI.

Skills required:
Proficiency in Python for data processing and automation.
Experience with Linux operating systems and shell scripting.
Basic knowledge of computer architecture or hardware performance (e.g., GPU/CPU utilization).
Strong analytical skills and attention to detail in experimental data collection.

Research area: My research focuses on applying remote sensing (existing and novel datasets, including SAR imagery, altimetry, optical, GNSS and their integration) and geospatial methods to study wetlands, freshwater habitats, and cryospheric environments. I work with multisource spatial data to characterize environmental features, monitor ecosystem change, and support geodesy‑based analyses of climate‑sensitive regions.

Student roles:
The student will contribute to the preparation, processing, and analysis of geospatial datasets. Their role includes harmonizing multisource data, supporting the annotation process, and assisting in the detection of key environmental features. They will work with satellite imagery, participate in preliminary algorithm evaluations, and help refine analytical workflows. The student will also be introduced to novel satellite datasets and may take part in field activities. Methods will tested on two geographical areas.

Skills required:
The project requires a student with foundational knowledge in geomatics, remote sensing, or environmental sciences. Skills in GIS and programming (Python or R) are desired. Familiarity with satellite imagery and an interest in fieldwork are considered assets. The ability to work independently is also important, even though regular supervision will be coordinated from the start of the internship.

Research area: Our group works at the intersection of multimodal learning and AI safety, with a particular focus on the detection, understanding, and explanation of harmful online content. We study how large multimodal models can be adapted to reason about content that spans visual, auditory, and textual modalities. Our current research centers on the video domain, where harmful meaning frequently arises not from any single modality but from the interaction between them, and unfolds over time rather than within isolated frames. We are also interested in the generative side of this problem, both as a tool for moderation and as an emerging

Student roles:
The intern will function as a contributing member of the research group, working closely with senior researchers and the principal investigator on a focused research project within the broader hateful video moderation initiative.

Specific responsibilities will be tailored to the intern's background and interests, but typically include: Reviewing relevant literature in multimodal learning, video understanding, and AI safety to identify open problems and methodological gaps. Implementing and running experiments on large multimodal models, including data preprocessing, model fine-tuning, evaluation, and error analysis across multiple datasets. Designing or contributing to new evaluation frameworks, benchmarks, or annotation pipelines as needed. Participating in weekly group meetings, presenting progress updates, discussing intermediate results, and incorporating feedback from peers and supervisors. Documenting experimental procedures, maintaining reproducible code, and ensuring that experiments are reproducible and well organized.

Throughout the internship, the intern will receive close mentorship and will be supported in developing both technical research skills and academic communication skills. The intern will have access to the group's computing resources and will be encouraged to engage deeply with the research process, from problem formulation through to experimentation and analysis. The internship is intended to be substantive and immersive, providing a realistic preview of graduate-level research in multimodal AI.

Skills required:
The ideal candidate is an undergraduate student in Computer Science, Artificial Intelligence, or a closely related field. Strong programming skills in Python are essential, along with hands-on experience using deep learning frameworks such as PyTorch. Familiarity with transformer-based models, vision-language models, or multimodal learning is highly desirable. Beyond technical skills, we look for candidates with intellectual curiosity, attention to detail, the ability to work independently, and a genuine interest in AI safety and responsible technology.

Research area: Robot autonomy requires robust environment representations. Dense maps consume memory. Semantic graphs lack geometric detail. This project combines 3D Gaussian Splatting (3DGS) and 3D Scene Graphs (3DSG). 3DGS provides dense geometry. 3DSG provides semantic logic. This hierarchy creates a self-adaptive map representation. Robotic systems can use this structure for geometric queries and affordance inference.

Student roles:
- Implement a baseline robot mapping architecture.
- Integrate vision foundation models to extract scene semantics.
- Develop a hierarchical framework combining scene structure and photo-realistic representations.
- Deploy the framework in ROS 2.
- Evaluate the representation for robot navigation.

Skills required:
- Proficiency in C++ and Python.
- Strong interest for robot navigation.
- Knowledge of 3D perception.
- Experience with ROS 2.

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 federated learning (FL) system that operates across multiple distributed participants. The role involves building components that support asynchronous training, where clients can train and update models at different times without strict coordination, improving system efficiency and scalability.

A key responsibility is implementing mechanisms for access control and secure participation, ensuring that only authorized clients can contribute to or query the system. The student will also explore how to design a broader FL ecosystem, including coordination layers (e.g., server or blockchain-based), participant management, and data/model sharing workflows.

The student will engage in hands-on coding, system integration, and experimental evaluation, measuring performance in terms of training efficiency, scalability, and model quality. They will work closely with graduate students, contributing to system design discussions and iterative improvements.

Overall, the role emphasizes systems building, distributed learning, and understanding how FL can support real-world AI applications in a scalable and trustworthy manner.

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 computer networking concepts is preferred but not required. Prior experience with a machine learning project is preferred. Interest in distributed systems and modern AI applications is a strong plus.

Research area: His research interests include blockchain technologies, publish/subscribe systems, massively multiplayer online games, and software-defined networking. Dr. Zhang's expertise lies at the intersection of distributed systems, networking, and data management.

Student roles:
Students are expected to read technical papers and documentation concerning targeted blockchain systems and applications. They must then compare various blockchain systems and choose the appropriate one for the project. The students will then design and implement their solution using smart contract programming languages over the appropriate open-source blockchain platform. Finally, the students will test and evaluate their system, and write a report with their observations.

Skills required:
Degree in computer science or software engineering
Experience in system research, deployment, evaluation
Strong programming skills in C++, Java, or Go
Familiarity with Ethereum (Solidity), Hyperledger (Fabric and Composer), other blockchain systems, and cryptography

Research area: The Fourth Industrial Revolution represents a fundamental change in the ways that we live and work. It explores the merging of the physical, digital, and biological worlds and fusing technologies in ways that create both benefits and apprehension. Cognification mimics human traits in non-human (e.g., robots) or augmented-human (e.g., machined-learned surgical visualisation) entities. Like electrical-grids, cognitive insights will, if not generated locally, arrive in pipes to accomplish the task at hand. Students will be forced to either become the highly exclusive knowledge-creators that machines haven’t mastered, yet, or become the knowledge-sponsors who search/sift/apply and take-responsibility for role-specific pieces of knowledge.

Student roles:
The interns will read a set of articles, discuss key points, debate counteracting ideations, synthesize their view, and write them for the consumption of open-access publication avenues such as 'The Conversation' (theconversation.com/ca). The interns will also explore targeted 4IR technologies.

Skills required:
Students will be asked to explore a range of contemporary 4IR skills that higher education students graduate with, across multiple domains, and analyse whether a) these skills will be impacted by cognification, and b) if so, how to up-skill students to complement their skills to align with cognification. Mitacs interns should be prepared to read, synthesize and write about the fourth industrial revolution and its impact on higher education.

Research area: My research focuses on artificial intelligence, large language models, human–AI interaction, affective computing, and AI in education. I study how AI systems can support learning, feedback, user modeling, and responsible decision-making in human-facing contexts. My work combines machine learning, natural language processing, evaluation of LLM-generated responses, learner modeling, and human-centred AI design. This project builds on my expertise in trustworthy educational AI, programming education, affect-aware computing, and the design of AI systems that support learning without replacing students’ cognitive effort.

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 LLMs in education, AI-based feedback, intelligent tutoring systems, programming education, and responsible AI. The intern will help define the project scope and develop a small set of synthetic programming-help scenarios representing common student needs, such as debugging, conceptual confusion, code explanation, and requests for complete solutions.
In the second stage, the intern will design a pedagogical response-policy taxonomy. This taxonomy will specify when an AI agent should ask a Socratic question, provide a small hint, explain a concept, identify a misconception, give feedback on partial code, or avoid producing a complete submission-ready answer. The intern will 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 successes and failure 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 student records, grading decisions, sensitive personal data, or live course deployment. The project is designed as a focused 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, software engineering, artificial intelligence, data science, or a related field. Required skills include Python programming, basic programming knowledge, ability to read research papers, and clear written communication. Experience with large language models, prompt engineering, APIs, natural language processing, data annotation, evaluation metrics, or human–computer interaction is an asset but not required. The student should be careful, organized, and interested in responsible AI, programming education, and designing AI systems that support learning rather than replace student effort.

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: I lead the Communication Networks Laboratory in the School of Engineering Science at Simon Fraser University: http://www.ensc.sfu.ca/~ljilja/cnl/index.html. My research interests span two areas: nonlinear circuits and systems and communication networks. In the area of nonlinear circuits and systems, my research deals with: analysis of complex systems and networks; theory of nonlinear circuits and systems; software tools for circuit simulation, and homotopy methods for finding dc, steady-state, and transient solutions of transistor circuits. I have developed and implemented homotopy algorithms for finding dc operating points, steady state, and transient responses of electronic circuits.

Student roles:
Student will collaborate on the NGSPICE open source project available at http://ngspice.sourceforge.net/. The project involves enhancing the original UC Berkeley SPICE3 tools. NGSPICE is a mixedlevel/mixed-signal circuit simulator based on three open source software tools: SPICE3 (the circuit simulator developed by UC Berkeley), CIDER (a mixed-level simulator based on SPICE3f5 and the DSIM device simulator), and XSPICE (an extension to SPICE3 to include event-driven algorithms). NGSPICE tools are used to validate circuit designs by Universities and several IC CAD (integrated circuits computer-aided) companies. At the end of 12 week term student will: • Have good understanding of SPICE3 simulation tool. • Be able to implement theoretical concepts, methods, and algorithms used in analysis of circuit designs. • Enhance his/her computing skills and knowledge of programming languages. • Acquire analytical skills crucial in solving engineering problems. • Demonstrate knowledge of tools for integrated circuit design.

Skills required:
• The student should have research interests in circuit design and in developing computer-aided software tools for circuit analysis. • Background in the analysis and design of electric circuits, exposure to circuit design and circuit simulation tools such as SPICE. • Strong programming skills with ability to learn new programming languages and tools with minimum supervision. • Experience with C, C++, Matlab, CGI scripting, Java Script, and HTML is preferred. • Fluency in written/spoken English is essential.

Research area: My research is situated at the intersection of Artificial Intelligence, Games, and Human Computer Interaction. I seek to understand how complex interactive systems are understood, learned, and reasoned about, using the ways that people do those things to inspire new AI technologies that do the same. At the same time, I study existing AI technologies and use them to develop new methodical practices for interaction design. For further details about my research group, RISE (Reasoning for Interactive Systems and Experiences), please visit rise.csit.carleton.ca.

Student roles:
As explained above, the student will be responsible for designing and developing a software prototype that allows users to play abstract games and express their understanding of the mechanics of those games, saving those expressions into a database for later analysis. The work will involve reading and summarizing related research papers, as well as reading, understanding, and writing programming code, likely in JavaScript (for modifying Scratch) and perhaps also another language to facilitate database communication. The student will be expected to participate in weekly meetings with the supervisor and the supervisor's research group. The student will be expected to conduct a usability study of the software to assess its usability for novice users. Finally, the student will also be expected to document their work in the form of a final report, which will be used to support other students who continue to work on the project.

Skills required:
The student should be very familiar with programming, having programmed several projects from the beginning and with at least one including visual elements, and one including a database. The student should ideally be familiar with working in JavaScript. The student should be very comfortable reading and understanding English documents, and reasonably comfortable with writing them as well.

Research area: Dr. Fehmi Jaafar is interested in cybersecurity in the Internet of Things, in the analysis and the improvement of the security and quality of software systems, and in the application of machine learning techniques in cybersecurity.

Student roles:
1) Review and update of an intensive literature review in privacy and conformity.

2) Qualitative and quantitative data collection.

3) Domain characterization and preliminary metadata analysis (including the source of IP addresses, hosting servers, domain names, domain registration information, metadata from third-party sources, etc.).

4) Exploration of the use of Artificial Intelligence through a suite of machine learning techniques to enable privacy.

5) Specification of a Visualization system for privacy conformity.

6) Evaluation of the proposed approach for reporting purposes.

7) Publication of the results, including dissemination activities in national and international context.

Skills required:
- Knowledge of Python, C / C ++ and Java programming languages

- Knowledge of data security and cybersecurity

- Experience in machine learning

- Fluency in written and spoken French and English

Research area: Dr. Ji Hyun Ko is the Tier 1 Canada Research Chair in Neuroimaging and Neuromodulation. The overall theme of his research is the realization of “bench to bedside” via developing quantifiable brain imaging-based biomarkers which can be used for more accurate diagnosis and prognosis as well as an outcome measurement for novel interventions such as transcranial direct current stimulation. His current research topics include Parkinson’s disease, Alzheimer’s disease, Mental Health, and Epilepsy.

Student roles:
Students will be tasked with the brain imaging data analysis utilizing conventional statistics and/or machine learning. Students may also be involved in our on-going neuroimaging and neuromodulation studies if appropriate. Students are expected to database management, perform literature reviews, analyze data, interpret the findings, draft sections of research manuscripts.

Skills required:
Students must have background in computer science, computer/electrical/biomedical engineering, neuroscience, and/or psychology. Students with prior experience of conducting neuroimaging (PET and/or MRI) data analysis and/or machine learning will be prioritized.
We use SPM and CONN running on MATLAB. We also use custom-made MATLAB script and thus the ability to revise or create MATLAB scripts will be an asset. For statistical analysis, we use SPSS, but other software proficiency is also ok (e.g., R).

Research area: My research area is focused on the data analytics using Artificial Neural Networks (ANNs) on the sensor data coming from wearable sensors, smartphone, smartwatch, and so on. For Human Activity Recognition (HAR) using sensor data, my team has already implemented Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM, as well as transformer networks.

Student roles:
The primary research objective for this project is to perform HAR using data coming from sensors. To achieve this, the student is required to create an ANN-based model and validate the developed model by performing a comparison study with the existing state-of-the-art models.
1. A literature review of the HAR using ANN-based models:
This work involves conducting an exhaustive literature review of current studies related to HAR from sensor data using ANN-based models. This will include the implementing the state-of-the-art models and comparing them using a common benchmark dataset like UCI HAR dataset.
2. Development of an ANN-based model for HAR IoT data to detect human activity:
In this work, the student will develop an ANN-based model for the analysis of data sourced from HAR devices, with a particular focus on data coming from smartphones or skeleton data. The development process involves an in-depth exploration of performance metrics and available libraries to be used for the development. Key criteria for performance include precision and recall, time efficiency, resource costs, overall complexity, and other software and hardware dependencies.
3. Validation:
The developed ANN-based model will be compared with existing state-of-the-art models in the domain of HAR from sensor data or skeleton data. A meticulous comparative analysis will be conducted, employing the identified metrics, including precision, recall, specificity, accuracy, and F1-score. This methodology extends to the identification of existing gaps, serving as a foundation for future enhancements in the domain of HAR detection using ANN-based models. By systematically evaluating the developed model and pinpointing areas for improvement, this research seeks to not only contribute a novel solution but also pave the way for ongoing advancements and refinements in the detection of human activity from sensor data.

Skills required:
The student must have advanced coding skills using Python. The student should be able to work with Keras backend.
Preliminary knowledge in developing Artificial Neural Network (ANN) models is an asset.
Skill to adapt and learn new concepts and technologies is a must.

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:
The students will evaluate existing tools and frameworks for machine perception and interactive learning, as well as artworks that critically engage with AI. They will then develop a working prototype that allows users to observe and intervene in an AI system's interpretation of its environment, accessible to non-technical audiences. The student will work closely with the faculty supervisor and contribute to both the technical and conceptual direction of the project.

Skills required:
Students will be part of a team. We are looking for:
A computer science student with experience or strong interest in machine learning, computer vision, or interactive systems. Comfort with Python and familiarity with ML frameworks (e.g. PyTorch, TensorFlow, or Hugging Face) is essential. Experience with human-in-the-loop systems, active learning, or fine-tuning is a strong asset, as is an interest in research-creation and the ability to think about technical work in terms of user experience and public engagement.
A creative student -- could be in Education, Design, STS or other fields who can help design engaging installations, experiences, artworks

Research area: My research focuses on Human-Computer Interaction (HCI), Human-AI Interaction, Persuasive Technology, and personalization. I investigate how artificial intelligence and user-centred design can be combined to create adaptive, personalized, and ethically responsible digital experiences that improve learning, motivation, and behaviour change. My work integrates techniques from ML, NLP, recommender systems, and behavioural science to design and evaluate AI-enabled educational technologies. Current research projects explore personalized systems, persuasive educational interventions, AI-powered recommender systems, and educational gamification. I employ both quantitative and qualitative research methodologies, including user studies, sentiment analysis, and thematic analysis, with the goal of developing effective intelligent systems.

Student roles:
The following roles will be distributed between the two interns:
1) Systematic Literature Reviewer: The student will investigate existing Human-Computer Interaction (HCI) frameworks, specifically looking into Intelligent Tutoring Systems (ITS), adaptive gamification, and user profiling.
2) Recommender Systems Developer: The student's primary engineering task is to design, code, and test the algorithm that dynamically pairs a student’s skill index with problem constraints (e.g., matching a student struggling with "Arrays" to an optimal next-step problem).
3) Backend API Engineer: Working in the Node.js and Express.js backend, the student will implement new RESTful API endpoints and middleware to process real-time personalization logic, calculate adaptive outcomes, and push dynamic content changes.
4) Persuasive UI Integrator: Working within the frontend Next.js environment, the student will connect these backend recommendations to the user interface (e.g., updating the dashboard overview, modifying problem suggestions on the Problem Set page, or tailoring real-time toast notification rules).
5) Usability & UX Evaluator: The student will help orchestrate a simulated evaluation or a small-scale pilot study with undergraduate students. They will collect subjective feedback on system usability and intrinsic motivation factors (such as reducing cognitive anxiety or preventing repetitive boredom).
6) Technical Communicator: The student is responsible for documenting their newly introduced code modules to ensure full reproducibility. Finally, they will collaborate with the academic supervisor to summarize the project's algorithmic architectures and behavioural findings into a co-authored manuscript formatted for an international peer-reviewed HCI conference or journal.

Skills required:
The ideal candidate should have an undergraduate background in Computer Science, Software Engineering, HCI, or AI, with the following competencies:
1) Programming Proficiencies: Strong fundamentals in web app design and development (preferably JavaScript/TypeScript, Node.js/Express.js environments), and experience with NoSQL databases (preferably MongoDB).
2) Domain Intersections: Foundational knowledge or high interest in HCI principles, UX design, Recommender Systems, or Machine Learning (collaborative filtering, content-based filtering).
3) Soft Skills: High autonomy, experience tracking code using Git environments, and excellent technical writing capabilities to summarize algorithmic behavior.

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/Deep learning are considered a plus

Research area: My research investigates socio-technical aspects of software engineering, with emphasis on human–AI collaboration, responsible AI use, software development practices, and the integration of AI assistants into professional software engineering workflows. Research activities combine empirical software engineering methods with practical software development and human-centered investigation.

Student roles:
The student will participate as a research assistant within an active software engineering research group. Responsibilities may include reviewing literature, supporting data collection and analysis, assisting with empirical software engineering studies, developing scripts or small software prototypes, organizing datasets, and contributing to discussions about human–AI collaboration in software development. The student will work closely with graduate students and the supervisor through regular meetings and collaborative activities. Depending on the student’s background and interests, the internship may also involve exploratory experiments with AI-assisted software development tools and participation in research dissemination activities.

Skills required:
Students should have background knowledge in programming and software development. Familiarity with Python, Java, or web development is desirable. Interest in artificial intelligence, software engineering, or human-centered computing is expected. Experience with data analysis, software projects, or research activities is beneficial but not required.

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 play a key role in data collection, auditing, integration, and technical support for the development of hybrid Advanced Geothermal Systems (AGS) and hydropower models for northern communities. Working closely with datasets provided by Natural Resources Canada (NRCan) and other sources, the student will compile geological, thermal, hydrological, and community energy-demand data required for site-specific assessments.
A primary responsibility is data auditing and quality control, ensuring that all datasets are consistent, complete, and suitable for modelling. This includes reviewing temperature–depth profiles, subsurface properties, and infrastructure data, identifying discrepancies or uncertainties, and documenting assumptions. The student will also organize and standardize datasets into structured formats, ensuring traceability and readiness for simulation workflows.
The student will support modelling and analysis activities by preparing input datasets, assisting in simulation setup, and contributing to sensitivity analyses and interpretation of results. They will help link subsurface performance with system-level considerations, particularly in integrating AGS with hydropower to improve reliability and energy balance.
In addition, the student will work closely with NRCan collaborators, participating in meetings, coordinating data exchange, and documenting technical discussions. They will contribute to the preparation of technical summaries, figures, and reports, ensuring clear communication of data sources, methods, and findings.
Overall, the student’s role is to ensure that high-quality, well-audited, and well-documented data underpins all modelling and analysis, while actively supporting technical workflows and collaboration. This role is critical to enhancing the robustness, transparency, and credibility of the project outcomes.

Skills required:
The student should have a strong background in civil, geotechnical, petroleum, or energy engineering, with solid knowledge of fluid flow and heat transfer in subsurface systems. Experience in numerical modelling and simulation is essential, particularly using multiphysics or reservoir modelling tools (e.g., COMSOL or similar platforms). Familiarity with geothermal energy systems and basic hydropower concepts is advantageous. The student should be comfortable working with geological and field data and interpreting key subsurface properties. Strong analytical, problem-solving, and communication skills are required, along with the ability to work effectively in an interdisciplinary research environment focused on low-carbon energy development.

Research area: My specialized research area is wireless communications and intelligent network optimization. I focus on resource management (RRM), scheduling, and resource allocation in dynamic wireless systems under QoS, latency, and energy constraints. My work relies on mathematical modeling, optimization theory, and algorithm design to efficiently manage limited resources such as power, bandwidth, and time in large-scale networks. I also develop scalable solutions for time-varying and uncertain environments. More recently, I integrate machine learning and reinforcement learning methods to design adaptive and data-driven algorithms for wireless control and decision-making. Overall, my research combines wireless systems, optimization, and AI to enable efficient next-generation

Student roles:
The student will play an active role in the development, implementation, and evaluation of intelligent resource allocation strategies for next-generation wireless networks. Under the supervision of the research team, the student will first conduct a comprehensive literature review on resource management in wireless systems, with a focus on optimization techniques, machine learning approaches, and emerging quantum computing methods.
The student will contribute to the mathematical modeling of resource allocation problems (e.g., scheduling, power allocation, and user selection) under practical constraints such as latency, quality of service (QoS), and energy efficiency. Based on these models, the student will assist in designing and implementing algorithms using classical optimization, machine learning, and reinforcement learning techniques.
A key part of the role involves developing simulation frameworks to evaluate the performance of proposed solutions in dynamic and large-scale wireless environments. The student will analyze and compare the effectiveness, scalability, and energy efficiency of different approaches. Additionally, the student may explore preliminary concepts related to quantum or hybrid quantum-classical algorithms for solving complex optimization problems.
Throughout the project, the student will document their findings, contribute to technical reports, and participate in regular meetings to present progress. The student is expected to demonstrate initiative, critical thinking, and the ability to work both independently and collaboratively. This role provides hands-on experience at the intersection of wireless communications, optimization, and artificial intelligence, preparing the student for advanced research or industry applications.

Skills required:
The student should have a strong background in electrical engineering, computer science, or a related field, with knowledge of wireless communications or networking. A solid foundation in mathematics (probability, linear algebra, optimization) is required. Programming skills in Python, MATLAB, or C++ are essential for simulations and algorithm development. Familiarity with machine learning, particularly reinforcement learning, is an asset. Basic understanding of resource allocation and communication systems is desirable. Interest in emerging topics such as quantum computing is a plus. The student should demonstrate analytical thinking, problem-solving skills, autonomy, and good communication abilities.

Research area: Natural Language Processing (NLP) Machine Learning (ML) Artificial Intelligence (AI)

Student roles:
research
development
state of the art

Skills required:
Good programming skills ,
good background in ML, NLP

Research area: My research specializes in remote sensing and multi-sensor data fusion, with a focus on integrating hyperspectral, LiDAR, and optical data for environmental and geospatial applications. I work extensively in computer vision, object detection, and the development of advanced machine learning and deep learning models for image analysis. A core area of interest is applying quantum algorithms to remote sensing challenges, particularly in data processing, classification, and feature extraction. My work bridges cutting-edge AI techniques with real-world applications in forestry, climate monitoring, and agriculture, enabling more accurate, scalable, and intelligent analysis of complex Earth observation data.

Student roles:
Key responsibilities:
-Data Handling and Preprocessing: Acquire and prepare high-resolution satellite imagery for urban areas, including normalization, patch extraction, and labeling. Preprocessing may also involve extracting spectral indices or structural features to enrich input data.
-Model Development: Build a classical CNN architecture for land use classification using frameworks like TensorFlow or PyTorch. Integrate a quantum layer using IBM Qiskit, Pennylane, or TensorFlow Quantum to form a hybrid model. The student will explore different ways to encode classical image features into quantum circuits and test their effectiveness.
-Training and Benchmarking: Train the hybrid model on labeled datasets and compare its performance

Skills required:
The student should have strong proficiency in Python and experience with deep learning frameworks such as TensorFlow or PyTorch. A solid understanding of convolutional neural networks (CNNs), image processing techniques, and classification tasks is essential. Familiarity with remote sensing concepts and satellite imagery (e.g., Sentinel-2) is highly desirable. Basic knowledge of quantum computing and exposure to platforms like IBM Qiskit, Pennylane, or TensorFlow Quantum is an asset. The student should be self-motivated, capable of working independently, and eager to explore interdisciplinary research at the intersection of AI, quantum computing, and geospatial analysis.

Research area: Our research group explores human-computer interaction (HCI) by combining robotics, AI, and digital media, specializing in emotionally engaging experiences through socially interactive robots, including our water-based "Hydrones." Our work emphasizes LLM-driven behaviours, personality, and memory. Building on earlier research on single-robot, one-on-one interactions—including recent work using well-known characters to create adaptive robot personalities—this project expands to multiple robots, each with a unique personality, interacting with each other and groups of people in a shared environment. Our frameworks are designed to generalize beyond Hydrone to other social robot platforms, deepening understanding of how LLM-driven multi-robot systems shape group dynamics and community

Student roles:
The student's role will span multiple stages of the project's development, with tasks assigned according to their interests and expertise:

Simulation and System Integration: Develop and refine a Unity-based simulation featuring multiple LLM-controlled robot agents, including real-time communication with Python-based AI servers and data exchange for memory, personality, and coordination functions. The simulation will support both Hydrone and alternative robot embodiments.
Behaviour, Personality, and Memory Programming: Implement and customize LLM-driven behaviour modules so each robot exhibits a unique, story-inspired personality, remembers individual users, and responds appropriately alongside other robots, enabling engaging, lifelike interactions between robot collectives and groups of people.
Multi-Robot Coordination: Help design coordination mechanisms—turn-taking, role allocation, and shared attention—that allow multiple robots to interact with each other and several users at once in a coherent, socially appropriate way, in simulation and on hardware.
Multi-Robot Hardware Prototyping: Participate in building and programming new robots (primarily Hydrone units, with portability to other platforms in mind), adapting simulation-developed algorithms for real-world use, including assembling components, integrating sensors, and debugging.
User Studies and Interaction Design: Design and run user studies observing how groups interact with multiple robots, collecting and analyzing feedback on how personality, memory, and multi-robot behaviour shape group interactions and user experience.
Collaboration and Communication: Work closely with the team, attend regular lab meetings, and document progress for publication. Strong communication skills and interdisciplinary openness are essential.

Skills required:
The students should have creativity, problem-solving skills, and the ability to work in multidisciplinary teams. In addition, the student should have a number of the following skills:
• Familiarity with Unity (or similar software) and real-time simulation tools
• Experience with robotics hardware assembly and microcontroller programming
• Strong programming skills in Python and C/C++
• Understanding of AI/machine learning, especially LLM integration and prompt-based behaviour authoring
• Knowledge of sensor integration, computer vision, and memory architectures
• Familiarity with multi-agent systems or multi-robot coordination is an asset
• Experience with user studies and HCI is an asset

Research area: My research is focused on the overlapping of software supply chain security and AI. Modern software development often involves using AI tools. Such tools are built using a combination of existing software packages, frameworks and models from an enormous variety of sources. In order to understand the risks stemming from AI in software development, it is important to understand and characterize this diversity. Second, modern AI tools enable non-technical users, who may not have full understanding of security issues, to build complex software projects. It is thus important to understand how such tools can be used effectively without creating harm.

Student roles:
The student will join a research team that includes faculty members, graduate students, and other undergraduates from multiple institutions. Here's a detailed breakdown of what the student will do.

In the initial learning phase (2-3 weeks), the student will learn fundamental concepts related to software security and applied AI. This will involve reading tutorials and blog posts, and reviewing scientific papers with guidance. Throughout this phase, the supervisor will answer the student's questions, help the student learn new concepts, and provide simple exercises to support the student's understanding.

In the analysis phase (4-6 weeks), the student will work with graduate students and undergraduates to analyze the impact of AI tools on secure coding. The student will receive access to necessary tools and example tasks. Then, they will conduct several analyses independently, with support available from senior students and the supervisor as needed. At the end of this phase, the student will present their findings to the group.

In the solution development phase (3-5 weeks), the student will participate in brainstorming sessions to address the problems identified in the analysis phase. In open discussions, the team will define how to build detection techniques for insecure components. Then, the team will identify a set of possible solutions. After this, the student will work collaboratively to create mockup implementations of the proposed solutions. Finally, with the help of senior students, the student will design a user study to evaluate user practices surrounding AI tools for software development.

Throughout the project, the student will be expected to: learn from technical sources, including tutorials, blogs, and scientific papers; analyze software products for potential security issues; and participate in scientific discussions on how to solve the identified problems. This project provides a comprehensive learning experience in software security, analysis, and collaborative

Skills required:
The student needs to have working understanding of software development-related concepts: at least one programming language (preferably Python and/or JavaScript), and how to use programming IDEs such as Visual Studio Code. The student will also need to have basic familiarity with software security and AI concepts: what "malicious code" looks like, how AI models can be applied to coding, and similar.

Beyond that, the project just requires soft skills: curiosity about cybersecurity problems, and the willingness to learn new concepts, skills, and tools. The supervisor will be available to teach the student any concept that may be unfamiliar.

Research area: My specialized research areas are Software Quality, and Architecture, Debugging, Software Visualization, Virtual Reality/Augmented Reality, and I have been recognized as a pioneer and an international reference on Digital Games and Software Engineering. Further, he was the creator of Swarm Debugging, a new collaborative approach to support debugging activities. Finally, I developed new approaches to logging analysis using Machine Learning techniques.

Student roles:
The student role is to project, analyze and implement a prototype of VR Game IDE using C/C++ or Unit Platform on VR Platforms. The student will conduct small empirical
experiments to evaluate in short cycle project results and outcomes. Finally, the students have to elaborate a report to document their prototype and propose future improvements.

Skills required:
The students' required skills are programming and general concepts on Computer Graphics. Good communication skills, autonomy, proactive. The students have to be interested in Virtual Reality platform and video game development.

Research area: My research focuses on intelligent communication networks, Internet of Things (IoT), Age of Information, network science, and machine-learning-assisted resource allocation. I study how emerging wireless and cyber-physical systems can deliver timely, reliable, and efficient information under limited bandwidth, energy, and computing resources. My work combines simulation, data-driven modeling, graph/network analysis, and optimization-inspired algorithms for applications in IoT, edge computing, vehicular networks, cybersecurity, and next-generation communication systems.

Student roles:
The student will be responsible for implementing and testing a Python-based simulation framework for lightweight semantic communication in IoT systems. The student will first review basic concepts related to IoT data transmission, task-oriented communication, and simple machine-learning classification. They will then generate or prepare a small dataset, implement baseline raw-data transmission, and compare it with simplified feature-based or task-oriented transmission methods.

The student will run experiments under different communication conditions, such as noise level, reduced bandwidth, or limited transmission rate. They will evaluate performance using metrics such as classification accuracy, communication cost, delay, and robustness. The student will also create clear plots and tables to summarize the results.

The final role includes maintaining well-documented code, participating in regular research meetings, discussing progress and challenges, and preparing a short technical report and presentation. The student is not expected to develop new theory; the focus is on simulation, implementation, comparison, and interpretation of results.

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

Research area: My research focuses on intelligent communication networks, IoT systems, cybersecurity, network science, Age of Information, and machine-learning-assisted resource allocation. I study how connected devices, sensors, vehicles, and edge systems can exchange timely, reliable, and secure 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 communication systems.

Student roles:
The student will be responsible for implementing and evaluating a Python-based simulation framework for trust-aware security in IoT and vehicular edge networks. The student will begin by reviewing basic concepts related to IoT networks, vehicular edge systems, graph-based network models, and trust/reputation mechanisms.

The student will then build a simplified network model in which nodes represent sensors, vehicles, edge servers, or connected devices. They will simulate different types of node behaviour, including normal behaviour and abstract unreliable behaviour such as delayed reporting, message dropping, inconsistent data sharing, or poor forwarding reliability. These behaviours will be modeled only for defensive research and simulation purposes.

The student will implement several trust-evaluation strategies, such as simple reputation scores, neighbour-based trust assessment, and graph-based trust indicators. They will run experiments under different network conditions, including different node densities, percentages of unreliable nodes, communication ranges, and message reliability levels.

The student will compare the methods using metrics such as detection accuracy, false positives, false negatives, communication success rate, reliability improvement, and computational overhead. They will also create plots and tables, document the code, participate in regular research meetings, and prepare a final report and presentation. The role is implementation-focused; the student is not expected to develop new security theory or offensive cybersecurity techniques.

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, cybersecurity, graph theory, probability, or data analysis is an asset. The student is not expected to know advanced cryptography, formal security analysis, or mathematical optimization. A motivated student with experience in coding, simulation, and visualizing experimental results should be able to complete the project successfully.

Research area: My research is at the intersection of two sub-topics of artificial intelligence (AI), knowledge representation and reasoning, and autonomous agents and multi-agent systems. I study the logical specification of agents’ mental states (e.g. their beliefs, goals, abilities, etc.) and rational behaviour, and formal models of deliberation and reasoning within the multi-agent systems paradigm. Previously I have worked on formalizing prioritized goals and intentions, developing agent programming languages with declarative goals, and investigating agent communication. Recently, I have been looking at the problem of ``actual causation'', which involves finding the causes of an observed effect given a history of observed actions.

Student roles:
The student will spend the first 3 weeks getting familiar with the situation calculus and getting a crash course on other relevant background via directed reading. Another 3 weeks will allow the student to investigate previous work on actual causation. Finally, in the final 6 weeks, the student will spend time programming. By the end of the program, the student is required to develop a toolkit for reasoning about actual causes and possibly also a small technical report discussing their work. They are also required to present their work via a departmental seminar.

Skills required:
The student is expected to have excellent skills in formal logic, in particular in first-order logic. The student should also have prior experience with logic programming, such as Prolog. A record of excellent grades in relevant courses (such as discrete mathematics) is a prerequisite. Previous experiences with upper-level artificial intelligence/multi-agent systems/knowledge representation and reasoning courses and LLMs will be an asset. Finally, the student is required to have excellent communication and writing skills.

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 coding as an active and reflective learner.

1. Implement an AOP-based IDE extension with Spring AOP or AspectJ
2. Develop a trajectory program with the predesigned state-tracking algorithms.
3. Implement an interpretable agent that communicates with OpenAI API to present explicit and semantic trajectory-information.
4. Conduct pedagogical evaluation in a programming course comparing AOP-supported agents with traditional, unassisted collaborative environments.

Skills required:
Students in this project require an interdisciplinary background in computer science, human-computer interaction, computer engineering, software engineering, and other related subject areas. The candidates must possess:

1. strong programming skills, an understanding of AI and Large Language Model (LLM) architectures, and knowledge of learning technology.
2. strong foundational knowledge in Python, aspect-oriented programming with Python or Java, and experience in prompt engineering with LLMs.
3. a foundational understanding of multi-agent frameworks
4. a foundational understanding of self-regulated learning theories, computational thinking, and metacognitive processes like planning, monitoring, and evaluating

Research area: Fabio Petrillo is an associate professor at the University of Quebec at Chicoutimi. He obtained his Ph.D. in Computer Science in 2016 from Federal University of Rio Grande do Sul (Brazil) and was a postdoctoral fellow at Concordia University. Dr. Petrillo has worked on Empirical Software Engineering, Software Quality, and Architecture, Debugging, Service-Oriented Architecture, RESTful analysis on Cloud, Agile methods. He has been recognized as a pioneer on Computer Games and Software Engineering.e was the creator of Swarm Debugging, a new collaborative approach to support debugging, and he has developed new approaches to logging analysis using Machine Learning techniques.

Student roles:
In this research project, we will develop prototypes to evaluate an innovative way to integrate those new practices on software development. The main objective is to evaluate new constructors and frameworks to improve developers' experience. In addition, this project product will support students in the context of UQAC's programs.

Skills required:
The students' required skills are Pharo, Java, C#, C/C++, Python, JavaScript or any modern program language, and general concepts on Computer Science. Good communication skills, autonomy, proactive. The students have to be interested in software development and software quality.

Research area: My research interests and expertise primarily fall within a highly multidisciplinary field, Human-Computer Interaction (HCI), focusing on user experience, interaction design, and the design of interactive graphical interfaces for traditional desktops, multi-touch devices (smartphones, tablets), and Augmented and Virtual Reality (AR/VR) devices. HCI is a multidisciplinary field that focuses on the design, evaluation, and study of how humans interact with computer systems and technology. HCI combines principles and methods from computer science, psychology, design, and other fields to create user-friendly and effective interfaces and interactions.

Student roles:
As an intern, one of the primary roles of the student in this project is to learn and develop skills in the field of HCI. This project will provide the student with hands-on experience with UX design, usability testing, prototyping, and other relevant aspects of HCI. The student is expected to assist in conducting research to gather user requirements, market trends, and existing solutions. This can involve literature reviews, competitive analysis, user interviews, surveys, or other research methods. The student will be involved in designing wireframes, mockups, or interactive prototypes. Seek feedback from mentors and iterate on designs accordingly. Then, participate in usability testing sessions by assisting in test preparation, moderating sessions, observing users, and documenting their feedback. Analyze the test results and provide actionable recommendations to improve the project's outcome. The student should actively participate in team meetings, brainstorming sessions, and discussions. Share ideas, perspectives, and insights while respecting others' expertise. Maintain effective communication with team members, supervisors, and stakeholders to ensure everyone is informed about project progress and challenges. Finally, the student should be adaptable and open to learning new tools, methodologies, or techniques relevant to the project.

Skills required:
To successfully complete this project, a student requires a combination of the following skills and backgrounds. Understanding the principles of UX design is crucial in HCI. This involves creating interfaces and interactions that are intuitive, efficient, and enjoyable for users. Basic programming skills are required to implement interactive prototypes or to develop software applications for testing and evaluation. Familiarity with interaction design principles and research methodologies, and proficiency in conducting usability tests, collecting user feedback, and analyzing data, are vital for evaluating the effectiveness and usability of HCI projects.

Research area: Research in my lab focuses on the development and evaluation of technologies for rehabilitation of persons with physical disabilities. This involves virtual reality applications and simulations to promote the training of wheelchair skills. We incorporate notions related to serious games, motivation, motor learning and co-design to create applications and user interfaces that will lead to better, safer and more efficient use of wheelchairs.

Student roles:
Depending on interests and experience, the student will be responsible for the development of one or more of the enhancements described previously (hand tracking with computer vision, hand tracking with inertial sensor, field of view enhancement with projection screens or with head tracking). The student will also be responsible for implementing the solution in the miWe simulator.
Following this implementation phase, an experiment will be designed to evaluate its usefulness. To that end, we will recruit 10-12 participants, who will try the same simulated activity in the miWe, using either the current setup or the setup with the enhancement. We will then compare performance (i.e., speed, accuracy of task completion) in the two conditions. We will also collect comments from participants through short questionnaires and interviews, to compare their appreciation of the miWe with and without the added enhancement. The student will be involved in this data collection and analysis.
In addition, the student will be expected to meet regularly with the supervisor (i.e., weekly or by-weekly); to participate in monthly team meetings and in other activities at the research center (conferences, workshos, etc.).

Skills required:
- Unity 3D programming
- Arduino programming
- Interest in working in a team
- Interest in working on a project related to wheelchair training

Research area: Fabio Petrillo is an associate professor at the ÉTS Montreal. He obtained his Ph.D. in Computer Science in 2016 from Federal University of Rio Grande do Sul (Brazil) and was a postdoctoral fellow at Concordia University. Dr. Petrillo has worked on Empirical Software Engineering, Software Quality, and Architecture, Debugging, Service-Oriented Architecture, RESTful analysis on Cloud, Agile methods. He has been recognized as a pioneer on Computer Games and Software Engineering.e was the creator of Swarm Debugging, a new collaborative approach to support debugging, and he has developed new approaches to logging analysis using Machine Learning techniques.

Student roles:
The student role is to project, analyze and implement a prototype, extract and process data from software artifacts.

Skills required:
Google programming skills.

Research area: This project focuses on the application of Agentic AI to improve the quality, reliability, and maintainability of microservice-based systems. Modern software systems increasingly rely on microservice architectures, which introduce challenges related to service decomposition, dependency management, testing, performance, security, and evolution. Recent advances in AI agents offer new opportunities to automate software quality assurance activities and support developers throughout the lifecycle of microservice systems. This project investigates how intelligent agents can proactively detect, analyze, and resolve quality issues in microservice environments.

Student roles:
The student will contribute to the design, implementation, and evaluation of Agentic AI solutions for improving microservice quality. Activities include conducting literature reviews, analyzing microservice repositories, collecting software quality metrics, and developing AI agents capable of identifying and addressing quality issues.

The student will design and implement prototype agents that support activities such as architecture assessment, anti-pattern detection, automated refactoring recommendations, test generation, reliability analysis, and technical debt management. The project will involve empirical evaluations using real-world open-source microservice systems and software engineering datasets.

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 research dissemination activities.

Depending on project progress, the student may have opportunities to contribute to scientific publications and open-source software tools. This internship provides hands-on experience at the intersection of software architecture, distributed systems, AI agents, and software quality engineering.

Skills required:
Applicants should have a background in Computer Science, Software Engineering, Artificial Intelligence, or a related field. Knowledge of software architecture, distributed systems, cloud computing, or microservices is desirable. Experience with programming languages such as Java, Python, or JavaScript, software development tools, Git/GitHub, and machine learning techniques is an asset. Familiarity with software quality assurance, testing, or DevOps practices is beneficial. Strong analytical and problem-solving skills are expected.

Research area: Fabio Petrillo is an associate professor at the University of Quebec at Chicoutimi. He obtained his Ph.D. in Computer Science in 2016 from Federal University of Rio Grande do Sul (Brazil) and was a postdoctoral fellow at Concordia University. Dr. Petrillo has worked on Empirical Software Engineering, Software Quality, and Architecture, Debugging, Service-Oriented Architecture, RESTful analysis on Cloud, Agile methods. He has been recognized as a pioneer on Computer Games and Software Engineering.e was the creator of Swarm Debugging, a new collaborative approach to support debugging, and he has developed new approaches to logging analysis using Machine Learning techniques.

Student roles:
The student role is to project, analyze and implement a game prototype and a machine learning system using the state of art ML techniques as Keras or Apache Spark applied to video game testing. The student will conduct small empirical experiments to evaluate in short cycle project results and outcomes. Finally, the students have to elaborate a report to document their prototype and propose future improvements.

Skills required:
The students' required skills are Java, C#, C/C++, Python or R programming languages, and general concepts on Computer Graphics or AI. Good communication skills, autonomy, and proactiveness. The students have to be interested in video game development and software quality.

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:
To address the challenges described above, the student will develop and assess prototype interfaces to support improved collection of information characterizing the state of the power grid, relevant to event reporting and/or shift changes. These prototypes are likely to make use of a combination of speech, text entry, camera or screencapture input and will be assessed in simulation conditions with the assistance of other members of the team.

Skills required:
The student should have experience with interface design, preferably including an LLM back-end, and ideally have taken one or more courses in human-computer interaction.

Research area: Dr. Xiaoxiao Li is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) and CIFAR AI Chair at Vector Institute. Dr. Li is recognized as a Canada Research Chair (Tier II) in Responsible AI. Dr. Li's research aims to enhance the trustworthiness and efficiency of AI models, bridging the gap between cutting-edge AI research and practical real-world applications. Dr. Li's current interests include mechanistic analysis of large language and vision-language models (LLMs/VLMs), developing hypothesis-driven evaluations, and advancing methodologies toward artificial general intelligence (AGI).

Student roles:
The intern student will be assigned a detailed topic. The student is encouraged to explore the technical details during implementation. The student is required to attend regular project meetings during the internship. The intern student should report progress in the midterm and at the end of the internship.

Skills required:
The ideal student should be proficient in Python and have experience with Large Language Models and agentic frameworks. Familiarity with multi-agent systems and medical informatics would be a significant asset, although not strictly required. We welcome students from computer science/engineering, computational medicine, and adjacent domains who have a strong interest in medical AI research.

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:
Advancing our understanding of the evolution of Indo-European languages involves the application of a network phylogenetic model. This innovative approach allows us to effectively model the intricate patterns of language evolution by drawing upon the principles of horizontal gene transfer, an algorithm widely employed in detecting genetic exchanges. By adapting this algorithm to the realm of linguistics, we can discern and analyze instances of lexical borrowings that have transpired throughout the course of language evolution.

One of the primary objectives in employing this network phylogenetic model is to accurately estimate the rate of horizontal exchanges that have taken place between distinct language groups. By quantifying these exchanges, we gain valuable insights into the interconnectedness and interdependencies of different linguistic communities. This information not only contributes to our understanding of language evolution but also provides a comprehensive view of the cultural and historical interactions that have shaped the development of Indo-European languages.

Through the amalgamation of cutting-edge techniques and meticulous analyses, researchers can illuminate the complex dynamics of language evolution. By unraveling the intricate tapestry of borrowing, transfer, and exchange, we can shed light on the nuanced mechanisms that have shaped the rich linguistic diversity observed within the Indo-European language family.

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: My research focuses on the development of machine learning, deep learning, and evolutionary optimization techniques for analyzing complex biomedical data. A major area of interest is computational pathology, where artificial intelligence is used to extract clinically meaningful information from digital histopathology images. I also work on multi-modal data integration, combining pathology images with genomic, molecular, and clinical data to improve disease diagnosis, prognosis, and treatment prediction. My research emphasizes feature selection, model interpretability, and efficient machine learning methods for high-dimensional biomedical datasets.

Student roles:
The student will contribute to the development and evaluation of Genetic Programming (GP)-based models for cancer prognosis prediction using multi-modal data. Responsibilities include conducting literature reviews, preprocessing and analyzing histopathology image features, gene-expression profiles, and clinical data, and implementing GP and machine learning algorithms in Python. The student will design and perform computational experiments, evaluate model performance using appropriate statistical and machine learning metrics, and compare GP-based approaches with conventional methods.

The student will document experimental procedures and results, maintain reproducible code, and present progress during regular research meetings. They will also assist in interpreting the evolved models, identifying important predictive features, and preparing technical reports, conference presentations, and potential research publications. Through this project, the student will gain hands-on experience in genetic programming, machine learning, computational pathology, bioinformatics, and interdisciplinary biomedical research.

Skills required:
Applicants should have a background in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field. Strong programming skills in Python are required, along with familiarity with data structures, algorithms, and machine learning fundamentals. Experience with evolutionary computation, genetic algorithms, or genetic programming is beneficial but not required. Knowledge of machine learning libraries such as Scikit-learn, PyTorch, or TensorFlow is desirable. Students should possess strong analytical and problem-solving skills, be comfortable working with large datasets, and have an interest in artificial intelligence and biomedical applications. Prior research experience is an asset but not mandatory.

Research area: Gouri Ginde (Deshpande) is an Assistant Professor in the Department of Electrical and Software Engineering at the University of Calgary. Her research focuses on Data Analytics, Applied Data Science in Software Engineering, and Health Care. Gouri is also the Scientific Director of the Software Hub for AnalytiKs, Technology and Innovation Hub (SHAKTI) laboratory. The vision of her research lab is to solve software-centric problems that are usable in the real world. Gouri actively seeks collaborations with industry to bridge the gap between industry and academia and foster innovation.

Student roles:
Student will develop the tool to achieve the objective of the project (described above)
Host it on AWS and test it on the real-world dataset.

Skills required:
Software Engineering basics (Must have)
Programming, Use of Github co-pilot (Must have)
Experience using JIRA, Atlassian tools, Project management Data, reporting (Good to have)
Applied AI, Data munging, OpenClaw, MCP (good to have)

Research area: My main research area is mobile and wireless networks. More specifically I work on cognitive radio networks, machine learning-based solutions for wireless networks, multiple input multiple output (MIMO) systems, cybersecurity, and sixth generation (6G) networks. Based on my vast experience in cognitive radio networks (as it was my PhD dissertation topic), I am recently exploring its potential with intelligent reflecting surfaces.

Student roles:
Student 1 will develop and implement simulation models for IRS-CRNs including wireless channel modeling, data generation, and performance evaluation using tools Python and MATLAB.

Student 2: will design and evaluate deep learning-based optimization techniques, such as deep reinforcement learning, to predict optimal IRS configurations. This work will also improve spectrum efficiency, signal quality, and interference management in CRNs.

Both students will collaborate on testing, analysis, documentation, and presentation of research outcomes.

Skills required:
Students should have knowledge about R&D. Strong Python programming and mathematical optimization skills are required.

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:
* Assist in designing and implementing adaptive software fault prediction models.
* Collect, preprocess, and analyze multi-modal IoT–Cloud system data.
* Develop and evaluate machine learning and deep learning algorithms for fault detection.
* Conduct experiments, performance benchmarking, and result validation under realistic conditions.
* Support research documentation, report writing, and publication preparation.

Skills required:
* Strong background in machine learning, deep learning, and data analytics.
* Knowledge of software engineering, software testing, and fault prediction techniques.
* Experience with Python and AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.
* Familiarity with IoT–Cloud systems, streaming data, and distributed computing environments.
* Understanding of Explainable AI (XAI), data imbalance handling, and performance evaluation methods.

Research area: Internet of Things, wireless sensor networks, edge AI, and environmental sensing. This project extends an existing hybrid Zigbee–LoRa wildfire-monitoring platform by focusing on safe outdoor field validation, site-specific calibration, false-alarm reduction, and low-power operation for early wildfire-risk monitoring.

Student roles:
The intern will extend an existing research prototype rather than start from zero. Main tasks are: (1) prepare and test sensor nodes, gateway software, and logging tools; (2) run repeatable indoor/outdoor communication experiments with multiple node placements; (3) collect, clean, and analyze environmental and network data in Python; and (4) implement and evaluate modest improvements to the alert pipeline, such as persistence logic, multi-node agreement rules, or a lightweight benchmark model.

The intern will document experiments, visualize results, compare system behaviour across settings, and help prepare a final technical report and publication-quality figures. Depending on progress, the student may also support energy profiling under different reporting intervals and contribute to a supplementary comparison of low-complexity models for false-alarm reduction. The work is hands-on, interdisciplinary, and well scoped for a 12-week internship.

Skills required:
computer science, software engineering, electrical/computer engineering, mechatronics, or data science. Useful skills include Python, Arduino/C/C++, data analysis, basic machine learning, Git, and familiarity with sensors or wireless communication. The student should be comfortable working with both software and hardware and willing to participate in outdoor data collection

Research area: I work in intelligent autonomous systems. This involves developing the technology needed to enable machines to interact with people, and involves AI, computer vision, and robots

Student roles:
Software development.

Skills required:
Interest in computer science/autonomous devices. Interest in LLM's, AI and robotics.

Research area: My research interests revolve around smart radio environment with more emphasis on (i) interpretable machine learning for communication network, (ii) intelligent reconfigurable surfaces (IRS) aided communication and (iii) embedded lite AI models for low power internet of things. My research projects tackle the open issues on using different learning approaches in combination with different optimization techniques for efficient and interpretable end-to-end communication learning, and shade light on real time beamforming and IRS control with constraints dictated by the channel estimation overhead. Herein, machine learning is also considered as a potential enabler in realising the vision of IRS- empowered smart radio

Student roles:
The interns are expected to make a thorough survey on IRS-enabled communication from the following perspectives, (i) Path-loss and channel modeling, (ii) Constrained system design and optimization, and (iii) experimental assessments and testbeds. They will contribute to the design of an open source testbed while working on their respective tasks (i) and (ii).
In order to evaluate the performance limits of RISs in wireless networks, we need realistic models for the propagation of the signals scattered by RIS where it is necessary to relay upon physics-based models for the propagation of EM fields in closeness of the surfaces and to account for their circuit models in the problem formulation. In the other hand, for the development of accurate path-loss models, it is necessary to develop fading models for sub-wavelength structures, which need to be more easily integrated into communication theoretic frameworks.
When it comes to constrained system design and optimization, one of the potential novelties of RISs is the potential/assumption of changing the radio waves impinging upon them without the need of using power amplifiers and signal processing units. Unfortunately, such assumption introduces constraints on the operation of RISs. Usually, the restrictions on performing signal processing operations on the surfaces have an impact on the overhead that is related to gathering the necessary information for optimizing and configuring RISs. The fundamental trade-off between the complexity and power consumption of RISs, as well as the associated overhead needs to be carefully studied and evaluated.
Based on the current RIS- aided 5G communication network proof of concept, with the collaboration of PhD and Masters' students, the intern will contribute to enhacing the proof of concept and/or proposing EM and Ray Tracing based channel models.

Skills required:
1. Good theoretical background in communication theory
2. Good knowledge on electromagnetic and antenna theory with knowledge in CTS microwave and/or HFSS
3. Good programming skills in C/C++, Python and Matlab
3. Good skills in embedded systems programming
4. Intermediate level in scikit-learn and Keras frameworks

Research area: My research focuses on the intersection of 3D Gaussian Splatting (3DGS), instance segmentation, and generative modeling. I develop novel neural rendering techniques using 3DGS for efficient, high-quality 3D scene reconstruction and representation. By integrating instance segmentation methods, I enable precise object-level understanding in 3D environments, allowing identification and separation of individual instances within complex scenes. I also explore generative models to synthesize and manipulate 3D content, creating controllable generation frameworks. This interdisciplinary approach advances applications in AR/VR, autonomous systems, and interactive 3D content creation, bridging realistic 3D representation with intelligent scene understanding and generation.

Student roles:
3D Data Processing: Handle point cloud reconstruction using COLMAP, mesh processing in Blender, and data pipeline optimization
Performance Optimization: Implement efficient C++/CUDA solutions for real-time rendering and computation
Research Documentation: Conduct experiments, analyze results, and contribute to academic publications

Skills required:
Essential Technical Skills:
COLMAP: 3D reconstruction, Structure-from-Motion, camera calibration
Python: Machine learning frameworks (PyTorch/TensorFlow), OpenCV, 3D libraries (Open3D)

Preferred Background:
Blender: 3D modeling, mesh processing, Python scripting API
C++: Systems programming, real-time applications, graphics API integration
CUDA: GPU programming, parallel computing, algorithm acceleration

Graduate student or advanced undergraduate in Computer Science/Engineering with experience in computer vision, 3D graphics, or VR development.
Strong mathematical foundation in linear algebra and 3D geometry. Prior research in point cloud processing, instance segmentation, or interactive systems is highly valuable.

Research area: On the internet, graphic material such as maps, photographs, and charts are clear and straightforward to those who can see it, but not for blind or low-vision users. For them, graphical information is often limited to manually generated alt-text HTML labels, often abridged, and lacking in richness. This represents a better-than-nothing solution, but remains woefully inadequate. Artificial Intelligence technology can improve the situation, but existing solutions are non-interactive, and provide a minimal summary at best; the essential information described by the graphic frequently remains inaccessible.

Student roles:
Depending on the skillset and interests of the selected student, the role will include one of:

Apple iOS support: Many people who are blind do not have a laptop or desktop. A previous summer student worked on an iOS port, but it is not feature complete, and needs significant work before we can submit it to the AppStore.

Audio experiences: Design and implement richer auditory experiences, making use of audio libraries to represent elements in the image in a more compelling and descriptive manner than can be provided through text description alone.

Skills required:
Students should have strong software development skills, preferably with docker experience. Experience with haptic design, audio rendering, and ML frameworks is a bonus.

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 React and Node.js is preferred.

Research area: Machine learning is fundamentally transforming the interpretation of medical imagery. By utilizing advanced algorithms to decode complex geometric patterns, we provide clinical experts with the tools to identify anatomical anomalies with superior precision. Our core mission is to bridge the gap between computational innovation and clinical practice, leveraging data-driven insights to improve patient outcomes and save lives. Dates are flexible.

Student roles:
The student will advance state-of-the-art algorithms on image segmentation and data analysis, applied on cardiac or brain imaging.

To do so, work will be:
- Proposing and reading related literature,
- Designing, programming and validating algorithms,
- Preparing a research article.

The internship will follow these steps:
- Presentation at mid-internship and end-internship
- Week 1-2: literature review,
- Week 2-4: literature review and code design,
- Week 4-6: design of validation experiments,
- Week 6-12: validated code, draft of article if ready.

Communication will be based on:
- Weekly meetings within our group and one-on-one meeting,
- Weekly report on current status

Skills required:
The candidate should have strong computer programming skills, and adequate algorithmic and mathematical skills, all necessary to improve his creativity in designing new machine learning and computer vision algorithms.

Research area: Virtual locomotion is the technology that enables users to move within a virtual environment. The teleportation technique has been acknowledged as one of the most preferable and effective technique, which has been widely adopted in virtual reality applications. Teleportation usually allows users to move to a dynamic or pre-determined destination. The current dynamic destination teleportation technique only supports teleporting to a target that is within eyesight. Teleportation with pre-determined destinations allows users to travel to target locations that are far distance and contain obstacles in between. However, users will lose the capability to freely explore explore the virtual environment.

Student roles:
The students will become part of our XR research team and work with me and graduate students on our area of research. The students will mainly work on developing the software in Unity with C# or on local server side using python. The students will also collaboratively design user studies and, if time permits, conduct the studies and analyze the collected data and document the results. In addition, we hope that the student will have a great time visiting Calgary and the Canadian Rockies that are close.

Skills required:
The students need to have good software development skills. The students should be keen on software development using Unity and Large Language Model (LLM)/ Natural Language Processing (NLP). Some experience with the Unity or any other game engine is preferred, but this could also be acquired as part of the project. A curiosity for new Extended Reality (XR) technologies and software development is the key.

Research area: I am broadly interested in systems security, data privacy, operating systems, and distributed systems. My research involves building systems that address security and privacy challenges by design, and evaluating the security and performance properties of the system empirically. My current research directions include designing data security mechanisms in serverless and edge cloud platforms, and mitigations for software and hardware side channels in cloud applications. I am looking for students with strong software engineering skills. Experience with operating systems programming is desired.

Student roles:
The student will meet with the project group weekly and produce weekly progress reports to track the research progress. The student will read background literature on the topic, and will be involved in designing and implementing a research prototype, and performing empirical measurements of the prototype. Finally, the student will write a report on the research conducted during the internship.

Skills required:
The student must have done an undergraduate level course on operating systems by the time of internship. The student must be familiar with C/C++ programming. Familiarity with systems security concepts and programming languages like Python is highly desirable.

Research area: The laboratory aims to explore the paradigms of visualization and human-machine interaction that are made possible by new virtual reality and augmented reality (VR/AR) devices. The laboratory's work is applied mainly to two areas: 1) Medical interventions and 2) Immersive multimedia experiences. In the medical field, VR and AR tools aim to improve techniques for simulating, planning and guiding surgical interventions. In the context of immersive multimedia experiences, the laboratory's work focuses on the new paradigms of interaction and visualization opened up by AR and VR.

Student roles:
The intern's role is to create and integrate an existing computer vision library into an augmented reality application to perform 3D tracking of ARUCO markers. The intern will have to implement a mechanism that allows the markers' position and orientation to be easily retrieved in real time. They will also have to develop a simple AR application that allows the accuracy of marker tracking to be measured and the potential for interaction with these markers to be studied.

Skills required:
The candidate should have a background in computer science or computer engineering and/or strong programming and software engineering skills. They should be motivated to deepen their knowledge of 3D rendering, computer vision and human-computer interaction techniques and be willing to study relevant literature.
The candidate should have knowledge of the following tools:
* Game engines (Unity, Unreal Engine ou Godot) and their associated programming language (C#, C++ or Python)
* Computer vision libraries (ex. OpenCV)
* Graphics programming SKDs (OpenGL, Vulkan, DirectX, Metal, WebGL)

Research area: The research of our Medical Computer Vision and Robotics (MEDCVR) lab at the University of Toronto Mississauga is centered around research in the domain of computer and robot assisted medical diagnosis and interventions. We address challenges in the research field of medical technology, that have a focus on optical, spatial, cognitive or manipulative limits.

Student roles:
This Globalink Research Internship will include one (or both) of the following topics:
* Conducting simulated or hands-on experiments with medical robots
* Developing algorithms for medical robotics and computer vision
During the application phase, the skillsets of all participants will be analyzed and tailored subtopics for each student will be defined. The ultimate goal is to prepare the performed research for publication at a conference or in a journal.

Skills required:
Two or more of the following:
* Advanced programming skills (C++ / Python)
* Working hands-on with robots, surgical instruments, lasers, etc.
* Computer vision, graphics, and/or robotics
* Software libraries like OpenCV, libigl, ROS, etc.
* Augmented Reality
* Programs and development environments like NVidia Warp, MuJoCo, 3D Slicer, Unity, Unreal Engine, Sofa, Blender, AutoCAD (or similar)
* Machine Learning

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 act as a systems engineer and developer, focusing directly on resolving data fragmentation across our smart city fabric. The primary objective is to design and implement a standardizing Application Programming Interface (API) that unifies how diverse web and urban services publish and consume IoT resources.
Key responsibilities and expected deliverables include:
• Service Ecosystem Analysis: Analyzing and categorizing the operational profiles of diverse IoT-relevant web services, including authenticated platforms (e.g., Facebook, secure calendars), key-authorized endpoints (e.g., OpenWeatherMap), open public feeds (e.g., RSS), and our lab's native smart home/smart city cloud infrastructure.
• Standardized API Architecture: Designing a unified abstraction layer and API to standardize resource discovery, data exchange, and service access across these highly fractured protocols.
• Embedded Software Optimization: Developing and compiling the API and its underlying components specifically for resource-constrained, low-power hardware configurations.
• Prototype Deployment: Implementing a functional software prototype on a Linux-based Raspberry Pi platform to demonstrate real-time data interoperability and validation within the lab’s active Smart City environment.
Through this internship, the student will move beyond high-level application scripting to master low-level systems architecture, embedded optimization, and middleware design patterns. This role provides deep technical training in building resource-efficient, production-grade IoT infrastructure—skills that carry immense competitive value in both top-tier graduate research and advanced industrial R&D—while contributing directly to publishable smart city breakthroughs.

Skills required:
Core Qualifications:
• Research Motivation: High degree of self-motivation to tackle complex, non-trivial software engineering and data integration challenges.
• Programming Foundations: Strong software craftsmanship and programming proficiency, with core fluency in C++.
Technical Competencies & Assets:
• Practical knowledge of Web Services and standard API communication paradigms.
• Recommended familiarity with Software Design Patterns for writing scalable, maintainable architectures.
• Recommended experience operating in Linux environments and working with embedded systems hardware (e.g., Raspberry Pi, Arduino).

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 be responsible for:

- Conducting a literature review on efficient LLM inference, KV cache optimization, and attention sparsification techniques.
- Understanding the architecture and inference pipeline of transformer-based language models.
- Implementing baseline KV cache pruning methods from recent research papers.
- Designing and experimenting with novel token importance metrics or adaptive pruning strategies.
- Running experiments on benchmark datasets and evaluating the impact of pruning on memory usage, latency, throughput, and model accuracy.
- Analyzing experimental results and identifying trade-offs between efficiency gains and performance degradation.
- Documenting findings and prepare research publication for submission.

Skills required:
Students interested in this project should have:

- A solid foundation in machine learning and deep learning.
- Familiarity with transformer architectures and large language models.
- Experience programming in Python.
- Knowledge of deep learning frameworks such as PyTorch.
- Basic understanding of GPU computing and model inference workflows.
- Experience with experimental evaluation and data analysis.

Research area: My research interests span software development and computer science education. In particular I focus on applying AI to software development, as well as using AI to support personalised learning in educational games.

Student roles:
Successful applicants will work on developing a new block-based programming language and web-based development environment for use in Computer Science courses. Activities will include design and development of programming blocks, design and development of the development environment, and testing & debugging of block-based programs.

Skills required:
- Javascript and/or Python (required)
- Experience with block-based programming (e.g., Scratch) (preferred)
- Experience with LLMs (e.g., GPT, Claude) (preferred)
- GitHub experience (preferred)

Research area: Our group develops agentic AI for high-stakes decision-making: large language model (LLM) agents that interface natural language with formal tools (databases, simulators, mathematical optimization solvers). Building on our AGENTIQL framework (agentic text-to-SQL with adaptive routing, NeurIPS 2025 workshop) and on our work on calibrated, evidence-certified LLM systems, we study how agentic pipelines can translate operational questions into certified decision models, and recognize when they cannot. The aim is to bring formal verification and conformal-prediction guarantees to LLM systems; this is the research core of the operations co-pilot we are building with our airport and logistics partners.

Student roles:
The intern works within our agentic-AI team (two PhD students) with weekly 1:1 supervision and group meetings, owning the benchmark and the verification/abstention layers in stages. Weeks 1-2: study the text-to-optimization, tool-use, and conformal risk-control literature; reproduce a single-shot baseline on NL4OPT. Weeks 3-5: design and curate OpsOR-Bench (query taxonomy, schemas, reference models, automatic graders). Weeks 6-9: implement the formalize-solve-verify-repair pipeline, then the risk-controlled answer/abstain/escalate layer; run the main evaluation grid. Weeks 10-12: matched-compute centralized-versus-multi-agent ablation, error taxonomy, open-source packaging, and drafting of a co-authored manuscript; final presentation to the group and industry collaborators (public data only). The intern maintains rigorous experiment tracking, participates in code review, and presents twice in our AI-for-Decision-Making reading group.

Skills required:
Strong Python and software engineering (APIs, testing, Git); hands-on experience with LLMs (prompting, structured outputs, or fine-tuning); at least one course covering linear/integer programming or equivalent exposure to optimization modeling. Familiarity with a solver (Gurobi/HiGHS), agent frameworks, or conformal prediction is an asset. We value students who write careful evaluations rather than demos.

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. Collect and normalize CVE records, patches, and exploit data into a unified corpus.
2. Generate embeddings for the corpus and build a vector database for semantic search.
3. Design a retrieval-augmented pipeline that surfaces similar past vulnerabilities for new code samples.
4. Evaluate retrieval quality and compare the pipeline against a non-retrieval baseline.
5. Test incremental indexing so the system can ingest newly disclosed vulnerabilities.

Skills required:
Programming Competence – Python and familiarity with embedding models and vector similarity search methodologies
Security Domain Knowledge – Working familiarity with CVE, CWE, and CVSS classification frameworks
Analytical Skills – Capacity to design retrieval-quality benchmarks and interpret precision and recall trade-offs
Other Relevant Skills – Prior experience with at least one vector database, or demonstrated capacity to acquire such competence rapidly; familiarity with retrieval-augmented generation methodology

Research area: My current research area entails the testing of LLM safety and alignment through advanced red-teaming techniques that evaluate the resistance of LLMs to jailbreaking techniques.

Student roles:
The student would be in charge of reading relevant literature, gather and analyse data. The student will also contribute to experimental designs that will involve crafting examples of prompts and jailbreak attempts/algorithms that will be subsequently studied and analysed.

Skills required:
- Studying in computer science or software engineering or applied mathematics
- Good problem solving skills
- Some experience with data science and/or Python would be a plus

Research area: Our research focuses on AI and machine learning for cybersecurity in IoT and smart infrastructure. We develop intelligent intrusion detection systems that can detect cyberattacks efficiently and adapt to changing threats. Our current work includes AutoML, online learning, TinyML, explainable AI, and lightweight cybersecurity solutions for resource-constrained systems.

Student roles:
Two students will work collaboratively on the project, with shared responsibilities in literature review, dataset preparation, model implementation, experimental evaluation, and documentation. They will review related work on IoT cybersecurity, intrusion detection, AutoML, optimization, and LLM-assisted machine learning workflows. They will work on public IoT cybersecurity datasets, perform data preprocessing, and implement advanced ML models for attack detection.
Each student will also take a complementary focus area. One student may focus more on AutoML model development, and the other student may focus more on LLM-assisted optimization. Both students will run experiments, compare results using standard cybersecurity and machine learning metrics, and analyze the strengths and limitations of the proposed approach.
The students will participate in regular research meetings, discuss progress with the supervisor and research group members, and prepare final reports, code packages, and presentations summarizing the research outcomes.

Skills required:
Students should have basic programming experience, preferably in Python, and some background in machine learning, data analysis, and cybersecurity. Familiarity with classification ML models and data preprocessing. Interest in IoT security, AutoML, optimization, or large language models is strongly preferred. Students should be motivated to learn new tools, run experiments, analyze results, and document research progress clearly.

Research area: My specialized research area is affective data science, with a focus on the infrastructure needed to make emotion datasets more discoverable and reusable. I develop standards, schema-driven systems, and computational tools that organize complex emotion data into structured, searchable formats. This includes designing metadata frameworks for dataset documentation, building web platforms for schema validation and search, and studying how LLM-based extraction can convert unstructured research papers into standardized metadata records. The broader goal is to strengthen the technical foundations of emotion AI by improving dataset transparency, interoperability, and access, thereby supporting more rigorous, reproducible, and scalable research across affective computing.

Student roles:
The student will lead the technical implementation and day-to-day development of the project. First, they will review and evaluate candidate frameworks and database technologies for the proposed platform, with attention to suitability for schema-driven web forms, JSON Schema validation, backend integration, database search, deployment, and long-term maintainability. Based on this review, the student will recommend and justify a development stack for the D4ED platform, including frontend, backend, database, validation, and hosting components.

Second, the student will build the application using an agent-assisted development workflow. This includes creating and maintaining a version-controlled Git repository, organizing the project architecture, implementing the frontend and backend components, and ensuring that user-submitted dataset records are correctly validated against the D4ED JSON schema before being written to the database. They will develop the schema-driven web form interface, database writing and retrieval logic, and structured search/filter tools that allow users to identify and contrast datasets based on criteria such as modality, annotation type, size, human ratings, and licensing.

Third, the student will implement the automated paper-to-schema workflow. They will coordinate PDF ingestion, text parsing, backend LLM calls, structured result parsing, and form auto-population, ensuring that extracted records are reviewable by a human before final submission. They will refine prompts, output constraints, and validation logic to minimize malformed or incomplete outputs, and will document the extraction pipeline for reproducibility and future extension.

Finally, the student will contribute to testing, deployment, documentation, and dissemination. They will conduct structured testing of the application, code management, and help prepare technical reports or manuscripts from the work. The student will meet weekly with the supervisor to review progress. They must be able to work independently, incorporate feedback efficiently, ask precise technical questions when needed, and communicate implementation decisions clearly in both written and verbal form.

Skills required:
The student should have strong programming ability and a solid background in full-stack web development. They should be comfortable working with modern web frameworks, frontend and backend integration, and common web standards for building robust, user-facing applications. Experience with database design, querying, and backend data management is important, as the project involves storing, validating, and retrieving structured metadata records. Familiarity with agent-based development workflows is also required. Knowledge of large language models, structured extraction, and practical experience deploying or integrating LLM-based systems would be highly valuable, particularly for the paper-to-schema extraction component of the project.

Research area: My research focuses on scalable methods for large-scale optimization, with an emphasis on primal solution techniques and intelligent optimization systems. More specifically, I study the full optimization spectrum, ranging from optimization solvers to learning-based optimization proxies. This spectrum includes four interconnected perspectives: optimization solvers (commercial and open-source), metaheuristics (e.g., local search and simulated annealing), exact optimization methods (e.g., decomposition and branch-and-price), and machine learning approaches (supervised and unsupervised learning). My work aims to develop adaptive and computationally efficient methods capable of solving complex real-world optimization problems.

Student roles:
The student will contribute to the development of an AI-driven framework for automatic parameter configuration of mixed-integer optimization solvers. The internship will involve programming, computational experimentation, data collection, and machine learning model development.

The student will primarily work in Python and will help design automated experimentation pipelines for running optimization solvers such as Gurobi or IBM CPLEX, collecting solver performance data, processing solver logs, and organizing computational experiments.

A major component of the project involves implementing and testing an LLM-assisted troubleshooting module capable of analyzing solver logs and identifying promising subsets of solver parameters for tuning. The student will also contribute to the development and training of deep neural network (DNN) proxy models that predict effective parameter configurations from optimization instance features and historical tuning data.

Additional tasks include data preprocessing, feature engineering, visualization of experimental results, benchmarking on large-scale optimization instances, and assisting with documentation and research reporting. The student will participate in regular research discussions and collaborate on the evaluation of the proposed framework.

Skills required:
We are seeking a motivated undergraduate student with advanced Python programming skills and familiarity with large language models (LLMs) and modern AI tools. The student should have an interest in optimization, machine learning, and computational problem solving. Experience with scientific Python libraries such as NumPy, pandas, scikit-learn, or PyTorch is expected. Prior exposure to optimization solvers or mixed-integer programming is considered an asset, but advanced expertise is not required.

The ideal candidate should possess strong analytical and problem-solving skills, curiosity for research, autonomy, and motivation for interdisciplinary work at the intersection of optimization and artificial intelligence.

Research area: Our research focuses on knowledge-based voice user interfaces (VUIs) and natural-language interaction for immersive extended reality (XR) environments. VUIs allow users to interact with systems through spoken language instead of controllers, hand gestures, or menus. Recent advances in large language models (LLMs) create new opportunities for VUIs that can interpret flexible, natural utterances rather than relying only on predefined commands or less flexible rule-based parsers. We are particularly interested in applying existing natural language processing tools, including LLMs, to support voice interactions grounded in scene knowledge, object metadata, and spatial relationships in complex 3D environments.

Student roles:
The students will become part of our XR research team and will work with me and graduate students on the design, development, and evaluation of voice-based interaction techniques for immersive environments. Their main role will be to help develop prototype software in Unity using C#, as well as supporting tools and APIs using Python. This may include implementing Unity components for voice interaction, integrating LLM or NLU tools, extracting object metadata from XR scenes, building object knowledge graphs or datasets, along with developing and testing spatial reference resolution algorithms.

Skills required:
Students should have strong software development skills and an interest in XR, Unity development, and natural language human-computer interaction. Experience with Unity, C#, Python, LLMs, or other NLP tools would be valuable. Prior experience with Unity or another game engine is preferred, but motivated students can also develop these skills during the project. Familiarity with XR hardware, such as Meta Quest or Varjo XR devices, is beneficial but not required. Most importantly, students should be curious, self-motivated, and interested in building experimental software systems for emerging immersive technologies.

Research area: My specialized research area is AI-enabled cybersecurity for Internet of Things (IoT) and distributed edge-cloud systems. My work focuses on developing intelligent and privacy-preserving methods for IoT intrusion detection, threat prevention, federated learning, and secure system management. I'm particularly interested in lightweight AI, Large Language Models (LLM)-based security analysis, and federated learning methods that can improve the security, privacy, and reliability of general IoT environments without requiring raw data sharing.

Student roles:
The student will contribute to a software-based research project on AI-enabled security and management for distributed IoT environments. Each intern will be assigned to one of two related streams based on their background and interests.

One stream will focus on LLM- or agentic-AI-based IoT security analysis. The student will help collect and preprocess IoT logs, alerts, and network data, develop AI-based methods for threat interpretation, design simple guardrails or fallback rules, and evaluate the accuracy, latency, and reliability of the proposed approach.

The second stream will focus on federated learning for privacy-preserving IoT intrusion detection. The student will help implement baseline intrusion detection models, develop or test federated learning methods, evaluate performance under non-IID data and possible attack scenarios, and analyze accuracy, communication overhead, robustness, and privacy-related results.

All work will be software-based using public IoT cybersecurity datasets, simulation-based experiments, and reproducible Python prototypes. The student will also be expected to read related research papers, attend regular meetings, document code and experiments, prepare progress reports, and contribute to a final research report or academic publication.

Skills required:
Students should have a background in computer science, software engineering, cybersecurity, data science, or a related field. Strong programming skills in Python are required. Experience with machine learning, deep learning, IoT systems, computer networks, or cybersecurity is highly desirable. Knowledge of federated learning, intrusion detection, LLMs, edge computing, or privacy-preserving AI is an asset but not required. Students should be motivated to learn, able to work independently, and comfortable reading research papers, running experiments, analyzing results, and documenting their work clearly.

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 primary developer and researcher, translating advanced theoretical AI techniques into a functional middleware prototype. The focus of this role centers around evaluating and implementing LLM solutions optimized for runtime data streaming and real-time situational correlation.
Key responsibilities and expected deliverables include:
• LLM Evaluation & Selection: Investigating state-of-the-art Large Language Models and identifying an optimal, lightweight solution capable of executing continuous, incremental learning from daily user tasks.
• Prototype Implementation: Developing a modular software prototype incorporating the chosen algorithms, designed to smoothly interchange data and sync with the AMI-Lab’s active applications to dynamically correlate incoming environment metrics.
• Scenario Configuration & Testing: Structuring a comprehensive testbed scenario to simulate varied contextual tasks and generate diverse user activity variants to rigorously validate the system’s predictive accuracy.
• Scientific Reporting: Documenting the underlying research findings, architectural trade-offs, and deployment challenges into a peer-reviewed, paper-style technical report.
Through this internship, the student will bridge the gap between advanced data engineering and natural language reasoning systems. The position provides deep technical exposure to LLM fine-tuning/prompting paradigms, context-aware analytics, and ambient assistant software architectures—highly marketable assets for both prestigious graduate programs and corporate industrial AI research facilities.

Skills required:
Core Qualifications:
• Research Motivation: Exceptional self-motivation to investigate complex, open-ended R&D problems in real-world ambient ecosystems.
• Programming Proficiency: Superior software development skills with a strong command of foundational computer science principles.
Technical Competencies:
• Machine Learning: Practical experience or solid theoretical knowledge in core machine learning methodologies and predictive analytics.
• Web Services: Hands-on familiarity with modern Web Services, networking protocols, and standard API development communication frameworks.
• Systems Familiarity: Comfort or basic curiosity surrounding Large Language Model architectures, prompt engineering, or sequential/incremental learning pipelines.

Research area: My research focuses on artificial intelligence for adaptive network and service management, with emphasis on machine learning, reinforcement learning, and large language model guided optimization. I study how intelligent systems can improve resource allocation, orchestration, fault handling, and service performance in dynamic and distributed environments such as edge and cloud networks. My work also explores continual learning, decision support, and trustworthy AI methods that help networked systems adapt proactively to changing demands, operating conditions, and user contexts.

Student roles:
The student will contribute to the design and early development of an LLM supported framework for network and service management. Their role will include reviewing related literature, helping define system requirements, and participating in the design of the overall architecture that connects operational network data with an LLM based reasoning module.

The student will assist in identifying the types of data that should be collected from network and service management components, such as telemetry metrics, logs, and configuration states. They will help explore how this data can be organized into structured inputs that are suitable for processing by large language models. This may include preparing datasets, testing prompt formats, and evaluating how different representations affect the quality of the generated outputs.

The student will also support prototype implementation tasks. These may include developing software modules, connecting APIs or open source tools, and helping build a simple workflow that demonstrates how an LLM can analyze NSM related data and generate useful recommendations. Depending on the student’s background, the work may also involve basic experimentation, testing, and documentation.

Throughout the project, the student will work closely with me and contribute to regular discussions on design choices, implementation progress, and research directions. The role is intended to provide hands on experience in AI, software design, and networked systems research. By the end of the internship, the student is expected to contribute to a prototype, technical documentation, and a summary of findings that can support future stages of the research project.

Skills required:
Students should have a background in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related field. Programming experience in Python is essential, along with familiarity with software development and problem solving. Prior exposure to artificial intelligence, machine learning, data analysis, or networking is helpful but not strictly required. Students should be motivated to learn, work with research prototypes, and read technical material. Experience with tools or concepts related to LLMs, APIs, data processing, or system integration would be an asset, but the project is suitable for strong undergraduate students with good programming skills.

Research area: My specialized research area lies at the intersection of Artificial Intelligence and Intelligent Transportation Systems (ITS), focusing on developing advanced AI-driven methodologies to optimize urban mobility and enhance transportation safety, efficiency, and sustainability. I leverage Machine Learning, including Large Language Models (LLMs) and vision language navigation, to address complex challenges such as trajectory optimization, map-matching, and indoor-outdoor navigation. My research aims to foster innovative solutions for autonomous navigation, significantly contributing to smarter, more resilient urban environments.

Student roles:
The student will be responsible for conducting a comprehensive literature review on Vision-and-Language Navigation (VLN) benchmarks to identify primary sources of navigation confusion, such as linguistic vagueness, visual occlusions, and irregular spatial layouts, and will compile a detailed taxonomy of confusion types with illustrative examples. They will design and implement a semantic mapping pipeline that processes raw visual inputs, constructs structured scene graphs representing objects and spatial relations, and develops a graph-based planner to generate disambiguating waypoints whenever instructions prove ambiguous. Building on this foundation, the student will construct a multimodal fusion architecture that integrates convolutional neural network–derived visual embeddings with Transformer-based language encodings and spatial priors via cross-modal attention modules, and benchmark baseline performance to quantify how added spatial context improves instruction alignment and path-planning accuracy. Concurrently, they will fine-tune or prompt a large language model (LLM) to automatically detect mismatches between parsed instructions and the agent’s observations, generate corrective subgoals when confusion events occur, and implement a responsive interface for real-time querying of the LLM during navigation tasks. Subsequently, the student will integrate the spatial reasoning, multimodal fusion, and LLM-driven modules into a unified VLN pipeline, optimize end-to-end inference efficiency, and perform extensive simulation experiments in standardized environments such as Matterport3D to measure metrics including success rate, path-length efficiency, and confusion-resolution effectiveness. To validate generalizability, they will coordinate and execute a controlled indoor demonstration using a physical agent or simulated robot in a realistic testbed. Throughout the project, the student will meticulously document data schemas, model architectures, and code repositories, participate in weekly progress meetings with faculty advisors, collaborate with peer researchers, and prepare a final report that synthesizes methodologies, experimental findings, and best-practice recommendations.

Skills required:
Academic background in Computer Science, Robotics, Artificial Intelligence, Data Science, Electrical and Computer Engineering, or equivalent. Strong foundations in machine learning, deep learning, and data analytics, with proven proficiency in Python and frameworks such as TensorFlow, PyTorch, and Scikit-learn. Prior experience or familiarity with reinforcement learning (e.g., Q-learning), graph neural networks, or large language models (e.g., GPT) is highly desirable. Exposure to computer vision and natural language processing techniques—especially for multimodal fusion—is preferred. Experience with robotic platforms or edge compute units (e.g., Raspberry Pi) is an asset.

Research area: AI in education, adaptive learning, Cyber learning

Student roles:
Python or JavaScript programming, interface design, database management, and algorithm design.

Skills required:
Python or JavaScript programming, AI, database management, algorithms.

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:
- Train and evaluate deep learning models using LLMs for product similarity
- Combine text-based and image-based similarity to find relevant/related products
- Develop and implement an algorithm for finding related products
- Evaluate the LLM-based algorithms with real webshop products
- Write a final report with research questions answered

Skills required:
- Strong programming skills in JavaScript, Python
- Some prior working knowledge on training and testing LLMs
- Deep learning frameworks like Pytorch or TensorFlow
- DevOps / MLOps Skills with Docker, Kubernetes, and cloud (AWS, GCP, Azure)
- Some knowledge of recommendation systems in E-commerce
- Some natural language processing techniques, including text similarity
- Some deep learning knowledge for image similarity

Research area: In general, my research lies within Software Engineering and Computer Systems, with special interests in SE for and with AI, software performance engineering, and debugging and monitoring of distributed systems. In particular, I strive to conduct practical research, which provides contributions to both academia and practice in the area of designing, developing, testing and maintaining large-scale software systems.

Student roles:
The selected MITACS student will be the primary person responsible for this project. This student, who will be supervised by me and my graduate student(s), will gain first-hand experience in applying AI techniques to solve real world software engineering problems.

Skills required:
- Excellent analytical and communication skills

- Interested in large-scale software analysis and AI

- Comfortable with programming

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., setting up LLM APIs), setting up study environments using VMs to facilitate remote user studies, 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 LLMs, interest in learning new things, and genuinely motivated to work independently on research projects.

Research area: Our research group is dedicated to advancing safe robot autonomy in interactive open-world environments involving the coexistence or teaming of multiple robots and humans by (1) employing and advancing theoretical foundations of generative AI, distributed and game-theoretic optimization, and multi-agent learning for real-time robot autonomy while providing safety, performance properties, and computational efficiency; (2) bridging the gap between theory and real-world robot autonomy by harnessing the power of generative AI in decision making, perception, and prediction; (3) verifying the effectiveness of theorems and algorithms through hardware experiments.

Student roles:
The student should be able to write a code pipeline for multi-robot interactions using LLMs.

Skills required:
The student is required to have great mathematical and coding skills.
They should be comfortable with coding in python and working with Large Language Models.

Research area: Generative AI, Creative AI.

Student roles:
Implementation and documentation (will be named as co-author in associated publication).

Skills required:
computer Science, pytorch.

Research area: My research focuses on water resources systems management under changing natural and anthropogenic conditions. In particular, I develop hydrological and water resources management models to assess the impacts of climate change and climate variability on water availability, water demand, and water allocation. I am also interested in developing user-friendly decision-support platforms, such as web-based graphical user interfaces (GUIs), to effectively communicate research outcomes to stakeholders, water managers, and policy makers.

Student roles:
• Strong programming skills in Python; experience with MATLAB or R and GIS is considered an asset.
• Experience with data analysis and management of large environmental datasets.
• Background knowledge in watershed processes.
• Strong teamwork and communication skills.

Skills required:
Learn and apply the existing hydrological and lake management models developed by the research team (implemented in Python).
Compile, process, and quality-control historical and projected hydroclimatic dataset
Perform simulations of watershed inflows and lake water levels under historical and future climate scenarios.
Analyze and interpret model outputs, with a particular focus on flood risks, water-level management, and climate change impacts.
Contribute to the development of a decision-support and visualization platform for communicating model results and management alternatives.
Prepare a final report documenting methodologies, results, and recommendations.

Research area: Within the broader discipline of human-computer interaction (HCI), my research investigates, designs, and evaluates non-game forms of playfulness in software and electronic user interfaces. This includes interactions such as personalizing digital devices to facilitate self-expression, using software to play around with data analyses or visualizations, and engaging with joyful features like Easter eggs (hidden features or media).

Student roles:
The student will be both developing and evaluating game mechanics for livestreamed audience participation games (APGs).

On the development side, the student will use the Unity game engine to modify our existing testbed software to incorporate additional game mechanics. For example, our testbed currently supports various turn-taking modes, but we would also like assistance with implementing various ways for players to create synchronized collaborative moves and assists.

On the evaluation side, the student will assist with running user studies to compare the variants of the implemented game mechanics. This will involve running and streaming the experiment software over Twitch or Discord, providing instructions to participants, and helping to troubleshoot issues that arise during an experiment session (e.g., game screen recording, connection errors).

Skills required:
Required:
- Experience working with livestreaming tools as a host/streamer (e.g., Twitch, Discord)
- Experience developing with the Unity game engine, or experience with a similar game engine (e.g., Unreal) and a willingness to learn Unity
- Passion for game design

Beneficial, but not required:
- Experience designing video games (e.g., as hobby, for student club)
- Experience conducting user studies with human participants

Research area: My research focuses on using Artificial Intelligence and Machine Learning to solve real-world problems involving complex image data, especially hyperspectral images. I work on building models that can understand both the spatial and spectral information in data, using techniques like deep learning and transformer-based approaches. A key part of my work is making these models easy to understand and trustworthy, so their decisions can be explained clearly. I also explore new ideas such as combining classical AI with quantum tools like PennyLane. My research is applied in areas such as agriculture (crop monitoring), healthcare (medical image analysis), and document forensics.

Student roles:
The student will work as an active member of the research team and contribute to building models for hyperspectral image analysis using structured representations. Their role will include reviewing relevant research papers, preparing and organizing datasets, and implementing deep learning models in Python. The student will run experiments, evaluate results, and help refine the models to improve performance. They will also document their work, share progress during regular meetings, and assist in preparing a final report or research paper. The role focuses on hands-on learning, problem-solving, and collaboration within the lab.

Skills required:
The ideal student should have a background in Computer Science, Data Science, or a related field, with a basic understanding of machine learning and deep learning. Experience with Python programming is important, along with familiarity with libraries such as PyTorch or TensorFlow. Some exposure to image processing or data analysis will be helpful, but it is not strictly required. An interest in learning about hyperspectral imaging and working with structured data (such as graphs or spatial relationships) would be an advantage. Most importantly, the student should be curious, willing to learn new concepts, and comfortable working both independently and as

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 opponent-aware fine-tuning of decentralized multi-agent policies. 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 CTDE methods in MARL, with particular emphasis on opponent modelling techniques and their applications in decentralized execution settings. The intern will set up the necessary software infrastructure, including installing and configuring relevant MARL libraries and simulation environments, and reproducing results from key CTDE baseline algorithms such as MADDPG and MAPPO.

In the second phase (weeks 4–8), the intern will implement the opponent modelling component within the chosen CTDE framework. This involves designing and coding mechanisms for each agent to predict the behaviours of other agents using only local observations, and developing fine-tuning procedures that allow deployed agents to adapt their policies based on these predictions. The intern will run initial experiments to validate the correctness of the implementation and iteratively refine the approach based on results.

In the third phase (weeks 9–12), the intern will conduct a systematic empirical evaluation of the proposed framework across multiple cooperative and mixed-motive multi-agent benchmark environments. The intern will compare performance and adaptability against standard CTDE 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.

Skills required:
Applicants should have a solid foundation in machine learning and probability theory, with strong programming experience in Python. Familiarity with deep learning frameworks such as PyTorch or TensorFlow is expected. Prior exposure to reinforcement learning concepts (e.g., policy gradients, actor-critic methods, Q-learning) through coursework or independent study is highly desirable. Experience with scientific computing libraries such as NumPy and Matplotlib is required. Familiarity with game theory or multi-agent systems is a strong asset. Strong analytical thinking and problem-solving skills are essential, as the project involves both designing and empirically evaluating novel multi-agent reinforcement learning algorithms.

Research area: Tse-Hsun (Peter) Chen's research focuses on various aspects of software engineering, including log analysis, testing, and debugging. He emphasizes the integration of artificial intelligence (AI) for software engineering (AI4SE) and software engineering for AI (SE4AI). As the principal investigator of the Software Performance And Reliability (SPEAR) Lab, he has directed significant research efforts, contributed to numerous scholarly publications, and developed tools that significantly impact both commercial and open-source systems. SPEAR is one of the most active research groups in Canada and in the world.

Student roles:
The student needs to conduct research, either alone or be part of an existing project, under the supervision of Dr. Chen. The student will need to read related work from the literature and participate in discussion. The student will need to write up the research findings, with the goal of submitting to top academic conferences or journals.

Skills required:
The student needs good programming skills and should be familiar with Python. It would be great if the student had experience/knowledge about software development, machine learning, and using LLMs.

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 semantic knowledge graphs 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 design and implementation as an active and reflective learner.

• create and validate multimodal knowledge graphs (KGs) (for the domain knowledge) and cognitive graphs (CGs) for representing and tracing creative thinking.
• analyze a study case to augment the created CGs over the learning context scenario.
• analyze how the CGs-based approach impacts on the performance of human-AI collaboration for the AI-assisted teaching and learning analytics.
• use Neo4j to build and reason knowledge graphs
• use Neo4j LLM Knowledge Graph Builder to extract nodes and relationships from unstructured text

Skills required:
Students in this project require an interdisciplinary background in computer science, human-computer interaction, computer engineering, software engineering, and related subject
areas. The candidates must possess:

1. strong programming skills, an understanding of AI and Large Language Model (LLM) architectures, and knowledge of learning technology.
2. strong foundational knowledge in Python or alternative, knowledge graphs, and using Neo4j to build knowledge graphs,
3. a foundational understanding of multi-agent frameworks and agentic AI architecture
4. a foundational understanding of self-regulated learning theories, computational thinking, and metacognitive processes like planning, monitoring, and evaluating
5. deep understanding semantic analysis and knowledge graph embeddings

Research area: Our research area is at the intersection of Human-Computer Interaction (HCI) and Natural Language Processing (NLP), with a specific focus on low-resource languages. We are working on LinguaQuest Hokkien, a 2D RPG currently focused on Taiwanese Hokkien, a Southern Min language. By integrating NLP features such as text-to-speech, we aim to create an accessible and engaging platform for language learners. Through HCI user studies, we evaluate the usability, benefits, and limitations of these learning tools. The broader goal is to establish a framework for teaching through immersive gaming that can be extended to other low-resource languages.

Student roles:
The intern will help with development and assessment of the software.

Skills required:
Candidates pursuing a degree in Computer Science or related field.
Must-haves:
Passion for languages and language learning.
Knowledge and/or experience in designing database schemas.
Statistical skills for designing experiments and analyzing HCI study data.
Strong verbal/written communication skills for effective teamwork and academic writing.
Curiosity and openness to exploring new technologies and proposing innovative ideas.
Nice-to-haves:
Game development experience with Unity or the willingness to learn.
Multilingual background, offering valuable insight and experience applicable to language learning and cross-linguistic projects.
Knowledge of Hokkien.
HCI/Psychology background, with experience in running experiments and conducting user-experience testing.
Knowledge of Natural Language Processing.

Research area: The research area will bring insight into the socio-legal dynamics related to the emerging technology of social robots from: 1) the long standing history of the relationship between concerns about privacy, legal reform, and new technologies; 2) increasing sensitivity to the need to embed privacy into the design of emerging technologies; 3) increasing concern about loneliness and social isolation as a damaging by-product of modern life and culture; and 4) emerging use of social robots to, among other things, address loneliness, especially among seniors, and the companion emergence of consumer-programmable robots that will expand the applications these robots can perform.

Student roles:
The required role of the student would be tasked with pulling together materials for the literature review on these topics: social robots and privacy; loneliness as a social crisis; ethics of social robot design; and mobile applications and technologies addressing loneliness.

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

Research area: I am working in the intersection of computer science, software engineering and physics-based engineering. We use techniques from all of these fields to create the next-generation of certified engineering software. We also use techniques from other fields, such as grounded theory (which originated from social science and medicine), ontology and epistemology (philosophy), domain specific languages (mostly from CS, but with shades of linguistics) and 'theory presentations' (mathematics). From computing, we use multiple techniques from compilers, code generation and statically typed functional languages (such as Haskell).

Student roles:
The student would participate in all activities on Drasil (https://github.com/JacquesCarette/Drasil) -- coding of the infrastructure (in Haskell), coding of new examples (in Drasil itself), improving old examples when new commonalities and patterns are found, as well as cooperating with all others also working on this open source project. The students have significant scope for creative contributions; we heavily encourage the students to monitor their own 'pain points' while working on Drasil, suggest new features for mitigating those issues, and then design and implement them. We work in a modern, github-driven environment with Travis-driven continuous integration, large test suites and generation of code in many languages (such as Java, C#, C++, Python, and Lua). New backend languages will be implemented, with the students likely participating in that effort.

One of the best ways to understand the students' role is to look at what other students have done on the project itself -- everything is completely visible on the project's github pages. The workflow and scope of student work is entirely documented there, likely in more detail than anyone really cares to know. But this also means that we offer a 'no surprises' environment where what will be expected is fully visible and public.

Skills required:
We are looking for solid programmers who are very eager to learn new ideas. Someone who thinks they know all there is about programming will not be happy in our project, as we deconstruct all the usual methodologies of software engineering and turn them sideways. And we use ideas from many different other disciplines; while somewhat mathematics-centric, we also use a lot of physics and dip into knowledge management, philosophy and social sciences as necessary. Eagerness to learn, as well as re-learn new ways of thinking on old ideas, is very important.

Research area: I work in intelligent autonomous systems

Student roles:
Develop software infrastructure to support long duration vessel autonomy. Specifically to write ROS control software that incorporates known power level information and recharge time within the planning structure.

Skills required:
Interest in robotics. Knowledge of Python.

Research area: My lab develops low-cost wearable sensing tools and AI pipelines for long-term measurement of movement, sleep, fatigue, and embodied physiological state. We are interested in unobtrusive actigraphy devices that can record reliable multi-limb motion outside the lab.

Student roles:
Two interns will split the work into device prototyping and validation/analysis. Intern 1 will focus on hardware and firmware: selecting components, assembling prototypes, writing data-logging code, testing battery life and storage, and documenting build steps. Intern 2 will focus on data validation: designing bench and human-wear tests, comparing recordings against existing wearable devices, converting raw accelerometer data into usable files, and extracting actigraphy features. Both interns will maintain a shared Git repository, write setup instructions, and create plots showing signal quality and battery/storage performance. Final deliverables should include at least one working multi-limb prototype, firmware, sample recordings, Python analysis scripts, and a concise technical report.

Skills required:
Students should have interest in embedded systems, wearable devices, Python, C/C++, Arduino/ESP32-style development, electronics, or signal processing. Helpful skills include soldering, 3D printing or enclosure design, battery/power management, Git, data logging, and accelerometer/IMU analysis. Curiosity and careful testing matter more than prior hardware expertise.

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: Our research focuses on the design of immersive, interactive, and intelligent systems to support scientific discovery and data comprehension. We specialize in human-computer interaction (HCI), mixed reality (MR), and biomedical data visualization. Through the integration of machine learning, spatial interfaces, and tangible/mobile interaction techniques, our goal is to enhance how users interact with complex biomedical datasets. We are particularly interested in advancing analytic capabilities for genomics and precision health through interactive technologies that promote insight, engagement, and novel workflows.

Student roles:
The student will work collaboratively with our interdisciplinary design and development team including senior research associates and graduate students within the Synaesthetic Media Lab (Synlab) at Toronto Metropolitan University. Depending on the student's interests and experience, the role can include:
• Developing interaction modules such as gesture-based filters or mobile-driven text entry for genomic data search.
• Designing tangible object-based interfaces for mixed reality data exploration.
• Integrating machine learning models for mutation clustering and ranking.
• Implementing mobile-to-MR communication systems for real-time search and filtering.
• Conducting user experience evaluations to assess usability and effectiveness of new features.
The project will be modular and milestone-driven, ensuring that students can complete a meaningful and demonstrable contribution within the internship period. The student will gain valuable experience in MR development, HCI methods, biomedical data visualization, and interdisciplinary design.
The project is hosted in Synlab, an interdisciplinary research environment based at Toronto Metropolitan University, providing students with mentorship and potential for future academic or research collaborations.

Skills required:
This project is ideal for students with a background in one or more of the following areas: computer science, biomedical engineering, interactive computing, human-computer interaction, or data science. Familiarity with Unreal Engine, Unity, or other 3D game engines is highly desirable. Skills in gesture recognition, tangible/mobile interface design, or machine learning implementation (e.g., Python, TensorFlow) will be considered strong assets.
Students with experience in web or mobile development, especially for communication or remote-control purposes, and those with an interest in immersive analytics, will also find this project a good match. Comfort with experimental design and user evaluation methods is helpful,

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 machine learning developer and systems researcher, moving the project from theoretical algorithms to an operational smart city framework. The core responsibility lies in designing an intelligent layer capable of continuous, low-latency activity detection and sequential system updates.
Key responsibilities and expected deliverables include:
• Algorithmic Evaluation & Selection: Investigating state-of-the-art machine learning paradigms and choosing optimal, resource-efficient algorithms capable of continuous, incremental learning from daily user tasks.
• Middleware & Prototype Implementation: Developing a modular software prototype incorporating the chosen algorithms, designed to smoothly ingest and interchange streaming data with the AMI-Lab’s live applications to resolve situational ambiguities.
• Testbed Configuration & Simulation: Structuring a comprehensive test scenario to simulate varied contextual tasks, generating diverse activity variants to rigorously validate the system’s adaptive tracking and classification accuracy.
• Technical Scientific Reporting: Synthesizing the research outcomes, structural challenges, and deployment insights into a comprehensive, paper-style technical report.
Through this position, the student will master the realities of running machine learning models on real-world mobile and embedded edge topologies. The role provides exceptional technical training in data pipelines, adaptive streaming analytics, and ambient assistant software—highly valuable assets for both prestigious graduate research programs and corporate industrial AI labs—while directly advancing assistive smart city technologies.

Skills required:
Core Qualifications:
• Research Motivation: Strong self-motivation to navigate ambiguous, hands-on R&D challenges at the intersection of hardware and artificial intelligence.
• Core Programming: High proficiency in core software development practices and general computer science fundamentals.
Technical Competencies & Assets:
• Foundational experience with mobile application development.
• Operational familiarity with Linux environments and embedded systems hardware (e.g., Raspberry Pi, Arduino).
• Practical or theoretical knowledge of core Machine Learning algorithms.
• Understanding of Web Services and standard API communication frameworks.

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 or wireless communications, machine learning and programming.

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 result in a set of tangible outputs focused on machine learning model development, software infrastructure, and dataset contributions. Expected deliverables include fully developed and documented codebases for PyTorch-based machine learning pipelines, trained and validated models across detection, segmentation, yield estimation, and path planning tasks, and curated datasets including contributions to the CFSD initiative. Additional outputs will include experimental results and performance evaluations, prototype implementations demonstrating practical application within agricultural systems, and internal technical reports detailing methodologies and findings. Where appropriate, contributions to academic publications or presentations may also be produced. These deliverables are intended to support both ongoing research objectives and the translation of machine learning methods into practical agricultural applications.

Skills required:
The student should have a strong background in computational methods, including experience with data preprocessing, machine learning model development, and performance evaluation. Familiarity with programming, scripting, and maintaining scalable codebases is essential. The student should be comfortable working with multimodal datasets and adapting or improving existing model architectures, including tasks such as hyperparameter tuning and benchmarking. Experience with software integration and reproducible workflows is important. While primarily computational, the student should be open to field validation activities. Strong organizational, analytical, and communication skills are required, along with ability to provide regular progress updates and collaborate effectively within a research team

Research area: I lead the Communication Networks Laboratory in the School of Engineering Science at Simon Fraser University: http://www.ensc.sfu.ca/~ljilja/cnl/index.html. My research interests span two areas: communication networks and nonlinear circuits and systems.

Student roles:
The student is expected to work with large publicly available traffic datasets that are available for downloads from the RIPE and Route Views collection sites and implement models for detecting network anomalies and intrusions based on the existing machine learning algorithms. Preparing a report emanating from the research project is required. Help the research team in preparing content for possible conference publications is expected.The student is expected to be an active and contributing member of the research team within the Communication Research Laboratory (http://www.ensc.sfu.ca/~ljilja/cnl)
and actively participate in its daily activities. Attending (virtually) weekly group meetings is required.

Skills required:
The student should have research interest in machine learning. The candidate is expected to have strong programming skills with ability to learn new programming languages and tools with minimum supervision. Working knowledge of programming language such as Python and MATLAB is expected. Experience with machine learning algorithms and techniques is a plus. Fluency in written/spoken English is essential.

Research area: I conduct research in the area of learning sciences using machine learning algorithms. In educational psychology, I use machine learning algorithms to discover patterns in educational data. In computing science, I use machine learning to generate feedback automatically. In health sciences, I use machine learning algorithms to discover patterns in medical data.

Student roles:
The student will work with the graduate students in my lab to implement machine learning (ML) algorithms, compare and contrast various ML algorithms, interpret the results, and write up the findings.

Skills required:
The student needs a good command of Python or a desire to learn Python.

Research area: My expertise is in machine-learning-based algorithm design for wireless communication networks. The proposed algorithms are designed for providing efficient resource allocation in wireless networks. My research team and myself make use of our expertise and knowledge in Artificial Intelligence, Machine Learning techniques and mainly reinforcement learning, combinatorial optimisation techniques, heuristic algorithms, etc. to develop the new algorithms. We also use analytical tools, computer simulations or prototyping to evaluate the performance of the proposed solutions.

Student roles:
The student will perform, with the help and support of the supervisor, the following tasks:
- Perform a critical reading and analysis of few scientific papers in order to be aware of the state of art in the field of research.
- Participating in the conception and development of an algorithmic solution and the evaluation of the computational complexity
- Evaluating the performance of the proposed solution by simulations or by analytical tools.
- Writing a report (as a scientific paper) in order to present the proposed solution and obtained results.

Skills required:
Wireless Networking and Wireless communications. Evaluation of Algorithms complexity. Machine learning. Deep reinforcement learning, Combinatorial optimisation, Wireless Network simulations.

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, including radiotherapy. 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 play a key role in conducting the research activities of the project, contributing to all stages of the workflow. The primary responsibility will be to perform a comprehensive review of the scientific literature on radiation response, radiomics, and machine learning approaches for biomarker discovery in oncology, in order to identify relevant methods and datasets. The student will be responsible for identifying and acquiring suitable public datasets, such as clinical, imaging, and molecular data from established repositories (e.g., TCIA, TCGA). This includes data curation, preprocessing, and quality control to ensure usability for downstream analysis. A major component of the role involves developing and implementing machine learning models to predict patient-specific radiation response and treatment outcomes. The student will apply feature extraction and selection techniques, train and validate predictive models, and evaluate their performance using appropriate statistical metrics. The student will also be expected to interpret model outputs, with an emphasis on identifying potential biomarkers and ensuring clinical relevance. Finally, the student will document and summarize the research findings, and present results to the research team.

Skills required:
The student should possess solid programming skills in Python or R, along with experience using machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. Familiarity with data preprocessing, feature engineering, model development, and evaluation are beneficial.

Research area: My research focuses on artificial intelligence (AI) for wireless communications and networking, network security, and the Internet of Things (IoT) and Industrial Internet of Things (IIoT). By exploring new network architectures, designing advanced protocols, optimizing network resource allocation, and automating network management using analytical models and machine learning tools, my research aims to support emerging network use cases and significantly improve the capacity, performance, and security of networks in the upcoming 6G era.

Student roles:
Throughout the project, the student will meet with the supervisor weekly and participate regularly in group research discussions. The student’s role and a tentative schedule are outlined below:
• [Weeks 1–4]: Review relevant research on ISAC, particularly link-layer solutions, including outcomes from this and other research groups.
• [Weeks 3–6]: Study and experiment with multi-agent reinforcement learning and graph neural networks. Begin with simple experiments involving three ISAC nodes, and then extend them to a small-scale network with more nodes and a simplified MAC protocol.
• [Weeks 5–10]: Collaborate with the supervisor and team to establish a system model and develop a basic link-layer frame structure, access scheduling scheme, and resource allocation strategy, building on the review in Weeks 1–4 and the MARL and GNN experiments in Weeks 3–6.
• [Weeks 7–11]: Extend the experiments from Weeks 3–6 into a simulation platform for validating and testing the proposed advanced coordination mechanisms for ISAC. Conduct comprehensive evaluations and document the results.
• [Weeks 11–12]: Prepare a technical report summarizing the research conducted over the twelve-week period.

Skills required:
Medium Access Control and Scheduling: The student should have completed a course in Computer Networks or equivalent and have a solid understanding of wireless medium access control protocols, such as TDMA and CSMA/CA.

Machine Learning: The student should have a foundational understanding of machine learning through coursework or self-study, with hands-on experience implementing basic neural networks or reinforcement learning algorithms. Prior knowledge of multi-agent reinforcement learning and graph neural networks is preferred but not required.

Programming Skills: Proficiency in either Python or MATLAB, supported by substantial hands-on coding experience, is required.

Research area: Our research lies in the general field of process control, smart automation, and machine learning for energy, agri-food, and manufacturing systems, with an emphasis on optimization-based receding horizon estimation/control theory, dynamic transfer/reinforcement learning, and distributed parameter systems. The central goal of our research is to develop advanced model-based, data analytics, and machine learning theory, algorithms, solutions, and toolboxes for dynamic modeling, condition monitoring, soft sensing, fault diagnosis, state estimation, advanced control, and smart automation of energy, manufacturing, and agri-food systems towards enhanced efficiency, profitability, safety, and sustainability.

Student roles:
The candidate will be responsible for developing time-series data processing methods and machine learning-based classification models for fault diagnosis of water distribution networked systems. The candidate will have the opportunity to collaborate with our group members to work on real-world pipeline data and develop advanced data-driven and machine learning algorithms for enhancing the safety, reliability, and intelligence of next-generation water distribution networks. The student will assist in the algorithm design, implementation, and evaluation for the identification of pipe leakages, blockages, and physical sensor/actuator faults of pipeline systems. The project will follow the timelines and guidelines of the Mitacs Globalink Research Internship at the University of Guelph.

Through this project, the student will develop theoretical knowledge and practical problem-solving experiences/skills on process data analytics, machine learning, mathematical modelling, process monitoring and control, fault diagnosis, signal processing, and intelligent diagnosis of pipeline leakages, blockages, and physical sensor/actuator faults of water transportation systems. The student will also gain professional skills in teamwork, oral presentation, and technical writing. The student will have the opportunity to present the research outcome through a presentation, a poster, and/or a publication.

Skills required:
The student brings strong expertise in data analytics and machine learning, including time series data analysis, digital signal processing, Fourier transform analysis, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Variational Autoencoders (VAE), and Generative Adversarial Networks (GAN), supported by solid programming skills in Python (using PyTorch, TensorFlow) and/or MATLAB. A strong background in machine learning, optimization, state estimation, and process control methods is preferred. Experience and/or strong interest in numerical methods, dynamical modeling, condition monitoring, and fault diagnosis of water/energy transportation systems or related fields will be considered as an asset.

Research area: Introducing the LASSO laboratory. https://lassolab.org The LASSO lab is an academic research laboratory with expertise in Learning Algorithms, Statistical Software, Optimization. Machine learning is a branch of artificial intelligence that focuses on algorithms for big data. In the LASSO lab, we work in this field, with particular expertise in algorithms for the analysis and interpretation of scientific data, in areas such as genomics, neuroscience, medicine, the microbiome, cybersecurity, robotics, satellite/sonar imaging, and climate and carbon modeling.

Student roles:
Internship Experiences: The intern will work on a research project in machine learning.
This includes: developing new algorithms for the efficient analysis of large datasets (unbalanced classification, break detection, image segmentation, etc.); meeting with the professor weekly to discuss the project; attending weekly research seminars with the team to discuss various research projects; developing open-source software to support the research project; and writing research notes on GitHub or a blog, and potentially submitting a research paper to a conference or journal.

Skills required:
Skills required: interns should have studied statistics, machine learning, and have some programming experience, ideally with R or Python, using frameworks like mlr3 and torch. Best candidates will have a GitHub profile with evidence of regular contribution to various programming projects related to data analysis and machine learning. If you don’t have anything on GitHub yet, try reading https://animint-manual-en.netlify.app/ and doing some exercises.
Also please read my application guidelines https://tdhock.github.io/blog/2024/application/
and avoid copying from AI (otherwise your application will be ignored).

Research area: Our research lies in the general field of process control, smart automation, and machine learning for energy, agri-food, and manufacturing systems, with an emphasis on optimization-based receding horizon estimation/control theory, dynamic transfer/reinforcement learning, and distributed parameter systems. The central goal of our research is to develop advanced model-based, data analytics, and machine learning theory, algorithms, solutions, and toolboxes for dynamic modeling, condition monitoring, soft sensing, fault diagnosis, state estimation, advanced control, and smart automation of energy, manufacturing, and agri-food systems towards enhanced efficiency, profitability, safety, and sustainability.

Student roles:
The candidate will leverage real-world spectra datasets to develop machine learning-based predictive models and smart monitoring solutions for estimating quality variables of food manufacturing processes. The candidate will have the opportunity to collaborate with our group members to work on process data analytics and advanced data-driven and machine learning algorithms for enhancing the safety, reliability, and intelligence of food manufacturing processes. The student will assist in the algorithm design, implementation, and evaluation for quality variable online prediction of food manufacturing processes. The project will follow the timelines and guidelines of the Mitacs Globalink Research Internship at the University of Guelph.

Through this project, the student will develop theoretical knowledge and practical problem-solving experiences/skills on process data analytics, machine learning, mathematical modelling, process monitoring and control, and optimization of food manufacturing processes. The student will also gain professional skills in teamwork, oral presentation, and technical writing. The student will have the opportunity to present the research outcome through a presentation, a poster, and/or a publication.

Skills required:
The candidate brings strong expertise in machine learning, including principal component analysis, partial least squares, neural networks, transfer learning, and deep learning. The candidate is expected to have experience and/or interest in modeling, estimation, and optimizing food manufacturing processes or related fields, supported by solid programming skills in Python (using PyTorch and TensorFlow) and/or MATLAB. A strong background in machine learning, optimization, state estimation, and process control methods is preferred. Experience and/or strong interest in numerical methods, dynamical modeling, condition monitoring, and optimization of food manufacturing processes or related fields will be considered as an asset.

Research area: Our research lies in the general field of process control, smart automation, and machine learning for energy, agri-food, and manufacturing systems, with an emphasis on optimization-based receding horizon estimation/control theory, dynamic transfer/reinforcement learning, and distributed parameter systems. The central goal of our research is to develop advanced model-based, data analytics, and machine learning theory, algorithms, solutions, and toolboxes for dynamic modeling, condition monitoring, soft sensing, fault diagnosis, state estimation, advanced control, and smart automation of energy, manufacturing, and agri-food systems towards enhanced efficiency, profitability, safety, and sustainability.

Student roles:
The candidate will leverage multi-modal greenhouse data (including time-series measurements and images) to develop predictive models and smart monitoring and control solutions for estimating quality variables and improving water and energy usage efficiency of agricultural irrigation and fertigation systems. The candidate will have the opportunity to collaborate with our group members to work on real-world greenhouse data and develop advanced data-driven and machine learning algorithms for enhancing the safety, reliability, and intelligence of advanced agricultural irrigation and fertigation systems. The student will assist in the algorithm design, implementation, and evaluation for quality prediction and water and nutrient usage optimization of agricultural irrigation and fertigation systems. The project will follow the timelines and guidelines of the Mitacs Globalink Research Internship at the University of Guelph.

Through this project, the student will develop theoretical knowledge and practical problem-solving experiences/skills on process data analytics, machine learning, mathematical modelling, process monitoring and control, and optimization of agricultural irrigation and fertigation systems. The student will also gain professional skills in teamwork, oral presentation, and technical writing. The student will have the opportunity to present the research outcome through a presentation, a poster, and/or a publication.

Skills required:
The candidate brings strong expertise in machine learning, including physics-informed neural networks, transfer learning, deep learning, and reinforcement learning. The candidate is expected to have experience and/or interest in modeling and optimizing agricultural irrigation and fertigation systems or related fields, supported by solid programming skills in Python (using PyTorch and TensorFlow) and/or MATLAB. A strong background in machine learning, optimization, state estimation, and process control methods is preferred. Experience and/or strong interest in numerical methods, dynamical modeling, condition monitoring, and optimization of agricultural irrigation and fertigation systems or related fields will be considered as an asset.

Research area: My research lies at the intersection of mathematical optimisation, machine learning and mathematical modelling (in particular using probability theory).

Student roles:
The student will experiment with data, adjust and implement the machine-learning algorithm and work on integrating it into the matheuristic. If the project progresses as planned, the work will contribute to a research article to be co-authored by the student.

The project will be co-supervised by Prof. Ana María Anaya-Arenas and take place at UQAM, the Centre de recherche mathématiques (CRM) and the Group d'études et de recherche en analyse de décision (GERAD). The student will be able to participate in scientific activities in this rich environment

Skills required:
Required: basic familiarity with supervised machine learning

Required: basic familiarity with optimisation models.

Required: a good understanding of algorithmic thinking and strong knowledge of at least one programming language, preferably Python or C.

Since the student will need to implement optimisation algorithms, familiarity with a solver such as Gurobi or CPLEX would be desirable (but not required).

An interest in sustainability and real-world logistics applications is welcomed.

Research area: Dr. Masud Rahman specializes in the intelligent maintenance of software systems, including AI systems. He has been developing a comprehensive infrastructure towards Reliable AI by constructing novel fault taxonomies and benchmark datasets, generating insights through large-scale empirical and practitioners’ studies, and designing novel, intelligent tools. Dr. Rahman also blends AI and software engineering to tackle the maintenance challenges of conventional software and simulation modelling systems. He uses machine/deep learning, LLMs, Agentic AI, program analysis, and Information Retrieval to design intelligent tools and technologies and helps software and AI practitioners tackle various maintenance challenges.

Student roles:
The successful applicant is expected to execute several responsibilities during their stay at Dalhousie University. First, the student will conduct a research project under the direct supervision of Dr. Masud Rahman. (The feasibility of the proposed idea has already been analyzed, a limited literature review has been conducted, and potential risks have been identified.) The project has been divided into multiple milestones and deliverables, such as project proposal, progress report, final project demonstration, final project report, and replication package (e.g., source code, documentation). The student is expected to submit their deliverables by the deadlines for timely feedback and to present their completed work at the end of the internship, which might be disseminated.

Second, RAISE Lab holds weekly meetings (e.g., one-to-one, group meetings), chaired by Dr. Rahman, where each member discusses their progress and issues encountered. The student is expected to attend these meetings every week, present their progress report, and participate in the discussions. They are also expected to work on a provided computer in the lab. The student will be added to the official Slack/Teams channel of RAISE Lab for seamless communications. Our lab promotes and implements Equity, Diversity, Inclusion, & Accessibility (EDIA) principles in every aspect of student training, which includes mandatory onboarding training sessions (e.g., research, publication, campus life, code of conduct, culture of respect), peer support groups, and knowledge transfer. The student is expected to maintain collegiality and professionalism in all forms of communication during their stay at Dalhousie University.

Third, in addition to direct supervision by Dr. Rahman, a senior graduate student with an overlapping research interest might be assigned to the student as a mentor. The student is expected to work with their mentor to overcome the learning curves associated with the project and to develop a productive working relationship.

Skills required:
An eligible candidate is expected to meet three criteria. First, they should be in excellent academic standing during the application. Second, they should have completed several core CS courses such as Object-oriented Programming, Algorithms, Data Structures, Software Engineering, and Artificial Intelligence (i.e., essential to succeed in this project) and conducted at least one academic project. Any recognized certification in Agentic AI (e.g., Coursera) will be an asset. Third, the student should be hardworking, strongly motivated, inquisitive, and willing to learn new technologies. They should be able to work independently under Dr. Rahman’s mentorship and in a research team.

Research area: Dr. Mandal’s research focuses on the theme of medical image analysis using machine learning. Two research areas are being explored currently: (i) Medical image analysis and computer-aided diagnosis, and (ii) Laser scattering image analysis for biological cell identification. The proposed project falls under the medical image analysis and computer-aided diagnosis application.

Student roles:
The research intern will develop a CAD technique for melanoma detection using semi-supervised machine learning. The dataset will include both labelled, coarsely labelled and non-labelled histopathological image data. Convolutional neural network models have been developed using labelled data and supervised learning. These models will be extended to incorporate semi-supervised learning using the coarsely labelled and unlabelled data.

Skills required:
The student should have research interest in the areas of image analysis, and pattern recognition. Courses on image processing and artificial intelligence would be helpful. Concepts such as convolutional neural networks (CNN), autoencoders, and generative adversarial networks (GAN) would be helpful. Programming languages such as Python and MATLAB would be helpful for the software implementation.

Research area: This project focuses on developing reliable and explainable approaches for malware detection and localization (in particular, mobile malware). The developed approaches are based on a combination of program analysis and ML-based techniques. They are designed to help security analysts efficiently screen a large number of third-party software systems, providing well-reasoned explanations about suspicious software behaviors.

Student roles:
The student will help analyze existing work on improving the reliability of ML classifiers, adapt it to the mobile malware detection domain, and evaluate the effectiveness of the developed solutions. 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 malware samples and will use application analysis frameworks, such as soot or asm, for programmatically extracting their behaviors, which are used for classification. Finally, the student will explore the ability of LLM to assist with malware classification tasks.

At the end of the project, the student will be familiar with the ML reliability domain and will be able to analyze and discuss major ML reliability considerations. The student will also be familiar with LLM, mobile technologies, 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, Kotlin, 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;
- have a good understanding of mobile application environments (at least as an informed mobile user);
- be detail-oriented and thorough.

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

Research area: Matt Brehmer (he/him) is a human-computer interaction (HCI) researcher focusing on data visualization: he designs, implements, and evaluates new ways to communicate and collaborate around data. He is a member of the HCI Lab and the Future Cities Institute, and he leads the ubietous information experiences research (ubix) group. The continued relevance of our research is rooted in the conviction that some people have an intrinsic curiosity with respect to data and want to share this curiosity with others through the creative medium of visualization.

Student roles:
The student will join Professor Matt Brehmer's "ubietous information experiences" research group (https://www.ubixgroup.ca/p/join), part of the University of Waterloo's School of Computer Science Human-Computer Interaction (HCI) lab (https://hci.cs.uwaterloo.ca/). As a member of the group, the student will occupy a seat in the HCI lab and participate in weekly research group meetings. The student will also meet individually with Prof. Brehmer, at least once a week. The student is expected to prepare for these individual meetings and maintain a shared set of meeting notes, summarizing recent progress, next steps, and open challenges.

As far as the project is concerned, the student will be expected to engage in several different activities throughout the course of the internship. This task switching will require flexibility and effective time management. There will be an alternation between interface design, prototyping, and implementation activities will continue throughout the internship, with each activity producing artifacts and code maintained with version control systems. Alongside these activities the student will read and write academic literature and technical documentation. See the associated activities list for a sense of the expected frequencies of these activities.

The intended outcomes of this internship include: (1) a set of implemented demonstration scenarios that illustrate the potential utility of the gestural interaction; (2) implementations that can be extended and integrated into a human factors study application; (3) draft content for a research paper manuscript describing the project that can be extended and submitted to an HCI research conference or journal, with the intern positioned as the lead author (…finalizing, submitting, revising, and presenting the paper upon its acceptance at a conference will fall outside of the time frame of the internship).

Skills required:
1. Proficiency with extended reality (XR) application development, with a preference for augmented reality (AR) application development, such as experience with relevant development tools / frameworks including Unity, Unreal, WebXR, Android XR, and Apple visionOS and Reality Composer. 2. Familiarity with mid-air bimanual gestural interaction in XR. 3. Comfortable reading academic papers and technical documentation, along with a willingness to develop academic writing skills.

Research area: I specialize in making satellite radar imagery useful for the real world. My research develops AI-driven signal processing pipelines that clean, interpret, and extract meaning from Synthetic Aperture Radar (SAR) data imagery that sees through clouds, smoke, and darkness where optical satellites fail. Using deep learning, computer vision, and classical ML, I transform raw radar signals into actionable maps of flooded regions, deforested areas, and land-cover change. My award-winning DFT-based speckle reduction framework, validated across multiple satellite frequencies, forms the methodological foundation of this applied remote sensing research.

Student roles:
The intern will function as an active research contributor, not just a project assistant. Their core responsibility is to design, develop, and evaluate a deep learning pipeline for near real-time flood extent mapping using freely available Sentinel-1 SAR satellite imagery.
The role is structured across three progressive phases over the 12 weeks:
Phase 1 (Weeks 1 to 4): Onboarding and Data Preparation. The intern will familiarize themselves with SAR fundamentals, the Copernicus data ecosystem, and preprocessing workflows. They will download, clean, and prepare multi-temporal Sentinel-1 datasets from real flood events across multiple geographic regions, building a structured training dataset ready for model development.
Phase 2 (Weeks 5 to 9): Model Development and Training. The intern will implement and train deep learning architectures, including convolutional neural networks and attention-based models, for binary flood and non-flood classification. They will benchmark multiple architectures, tune hyperparameters, and evaluate model performance using standard metrics including accuracy, precision, recall, and F1 score.
Phase 3 (Weeks 10 to 12): Explainability, Visualization, and Writing. The intern will apply explainability techniques to generate interpretable visual outputs from the trained model, making results accessible to non-technical stakeholders such as disaster response agencies. They will document findings, prepare publication-quality figures, and contribute directly to drafting a peer-reviewed journal paper.
Throughout the internship, the student will attend weekly one-on-one research meetings with the supervising professor, maintain well-documented code in a shared GitHub repository, and present their progress in a final research presentation. The role is designed to give the intern complete ownership of a real research problem, producing a validated model and a co-authored publication as tangible outcomes of their 12 weeks at Thompson Rivers University.

Skills required:
The ideal candidate is an undergraduate student in Computer Science, Geomatics, or Environmental Sciences with Python programming skills and foundational knowledge of machine learning. Familiarity with image processing concepts is an asset. No prior experience with satellite data or radar imagery is required. The project is designed to onboard motivated students from the ground up. Strong analytical thinking, curiosity about environmental applications, and willingness to learn new tools are more important than a specific technical background. All software and datasets used are free and open-source.

Research area: My research group at UBC works on large-scale Internet measurement, focusing on detecting and characterizing Internet censorship and interference worldwide. We build longitudinal systems that collect network-level data across many countries, combining networking, systems, and applied machine learning to study how online information access is constrained in adversarial environments. A growing line of our work integrates natural language processing with network measurement, for example using language models to automatically generate and prioritize the domain and keyword lists used to test for censorship. Our goal is rigorous, reproducible measurement that supports Internet freedom and informs evidence-based policy.

Student roles:
The intern will be a hands-on contributor to an active research project and will own a well-scoped piece of it from start to finish. Their core responsibilities will be:

+ Literature and tooling ramp-up (weeks 1-2): read a small set of key papers on censorship measurement and NLP-assisted test-list generation, set up the development environment, and learn to run the group's existing measurement and data-processing tools.

+ Building the pipeline (weeks 3-7): implement and refine code that uses language models to generate and rank candidate domains and keywords across several languages and topics. This includes writing Python scripts, designing prompts or filtering heuristics, and validating intermediate outputs against known examples.

+ Measurement and analysis (weeks 6-10): run the selected candidates through the group's measurement platform to test for blocking across countries, then analyze the collected data to measure how much additional censorship the expanded lists uncover relative to existing baselines. The intern will produce clear figures and tables summarizing the findings.

+ Write-up and presentation (weeks 10-12): document the methodology, results, and limitations in a short technical report, and present the work to the research group.

Throughout, the intern will meet weekly with the supervisor for guidance, participate in group meetings, and work alongside graduate students who can offer day-to-day mentorship. The intern is expected to keep reproducible records of experiments, write clean and documented code, and raise questions early. The role is well suited to a motivated undergraduate who wants a genuine research experience and the chance to make a measurable contribution to a project on Internet freedom. Exceptional results may lead to co-authorship on a publication.

Skills required:
Applicants should have a solid programming foundation, ideally in Python, and be comfortable working in a Linux/command-line environment and with version control (Git). Coursework or project experience in at least one of the following is expected: computer networks, machine learning or natural language processing, data analysis, or systems. Familiarity with handling and analyzing datasets (for example with pandas or similar tools) is valuable. No prior experience with censorship measurement is required. Most important are curiosity, attention to detail, scientific rigor, and the motivation to work on a real-world problem at the intersection of networking, security, and machine learning.

Research area: I specialize in making satellite radar imagery useful for the real world. My research develops AI-driven signal processing pipelines that clean, interpret, and extract meaning from Synthetic Aperture Radar (SAR) data — imagery that sees through clouds, smoke, and darkness where optical satellites fail. Using deep learning, computer vision, and classical ML, I transform raw radar signals into actionable maps of flooded regions, deforested areas, and land-cover change. My award-winning DFT-based speckle reduction framework, validated across multiple satellite frequencies, forms the methodological foundation of this applied remote sensing research

Student roles:
The intern will work as an active research contributor with direct ownership of a real environmental problem. The role is structured across three progressive phases designed to build skills and confidence while delivering meaningful research outcomes within the 12 week internship.
Phase 1 (Weeks 1 to 4): Onboarding and Data Preparation. The intern will begin by building foundational knowledge of SAR and optical remote sensing through guided reading and hands-on exploration. They will then download and prepare Sentinel-1 SAR and Sentinel-2 optical satellite datasets from 2 to 3 real Canadian wildfire events using open-source tools. This phase ends with a clean, co-registered, analysis-ready dataset ready for model development.
Phase 2 (Weeks 5 to 9): Feature Engineering and Model Development. The intern will extract meaningful features from both satellite sensors, including radar backscatter values from Sentinel-1 and spectral indices such as the Normalized Burn Ratio from Sentinel-2. They will then train and evaluate a machine learning fusion model to classify burn severity across multiple levels from unburned to severely burned. Model performance will be validated against open ground-truth data from Natural Resources Canada. Deep learning approaches will be explored where time permits.
Phase 3 (Weeks 10 to 12): Visualization, Interpretation, and Writing. The intern will generate publication-quality burn severity maps, interpret results in the context of ecosystem recovery and disaster response, and contribute directly to drafting a peer-reviewed journal paper.
Throughout the internship the student will attend weekly one-on-one meetings with the supervising professor, maintain well-documented code in a shared GitHub repository, and deliver a final research presentation summarizing their findings. The role is designed to give the intern complete ownership of a meaningful research workflow, from raw satellite data to co-authored publication.

Skills required:
The ideal candidate is a senior undergraduate student in Computer Science, Software Engineering, Geomatics, or Environmental Engineering with working knowledge of Python and a basic understanding of machine learning concepts. Familiarity with image processing or GIS tools is an asset but not required. No prior experience with satellite data is necessary as the project is structured to onboard students from the ground up. Curiosity about environmental applications, strong problem solving ability, and willingness to learn new tools are equally important. All software and datasets used are free and open-source.

Research area: Learning Analytics is a process that formatively subjects continuously arriving data to statistical modelling and analysis to offer legitimate insights to improve effectiveness of student's learning experiences. Analytics is defined as an ethics-bound, autonomous, and trust-enabled human–AI fusion that measures and advances knowledge boundaries in human learning. It comprises of (a) the analysis and discovery of skill-acquisition traces from raw task-related data, (b) the discovery of new relationships among those traces, (c) the acquisition of new knowledge from networks of such traces, and (d) the use of such knowledge to direct training and instructional activities toward targeted outcomes.

Student roles:
The student will repurpose a set of existing math exercises in a Generative AI environment, simulate the problem solving processes of students with varying math competencies, simulate trace data on the growth of students' math competences, and analyze the resultant data to measure the progress of students' math competences. The student will be introduced to an in-house developed GenAI tool to personalize math instruction and feedback.

Skills required:
The candidates should have some background in mathematics. Exposure to Generative AI technologies, in the context of math education, prior to their arrival in Canada will be an advantage.

Research area: Computer communications and networks, especially protocol design, performance evaluation and applied network security.

Student roles:
The intern student will work with faculty members and graduate students, learning through the research process and helping with measurement, simulation and simple prototyping, which will lead to publishable work with technology transfer potentials. For more information about the research projects, the related projects and the experience of the former MITACS interns, please refer to http://web.uvic.ca/~pan and the references therein.

Skills required:
Interest and basic knowledge in computer networks, and programming skills in C/C++ and Python, are expected. Experience with network measurement and simulation tools such as ping, traceroute, iperf and ns-2/3 is a plus.

Research area: My research focuses on conceptualizing and measuring the unintended harms of AI in education. As AI tools spread through learning platforms and institutional analytics, the field has many harm taxonomies but few rigorous instruments to actually measure harm in practice. I work at the move "from taxonomies to instruments"—using psychometrics, learning analytics, and educational data mining to define harm constructs precisely and measure them validly. A central methodological challenge I study is discriminant validity: distinguishing genuine harm (e.g., over-reliance) from surface-similar but benign behaviours such as adaptive help-seeking. My goal is evidence educators and designers can trust.

Student roles:
The student will be an active member of the research team, contributing to the design and validation of a harm-measurement instrument. Responsibilities may include:

Construct definition: helping sharpen the definition of one harm construct and its boundaries against benign or strategic behaviours (e.g., adaptive help-seeking, gaming-the-system).
Codebook development: building a multi-indicator measurement codebook, applying it to interaction traces, and refining it through iteration.
Reliability and validity work: computing and interpreting inter-rater reliability, examining discriminant validity, and honestly documenting where the instrument is and is not yet trustworthy.
Literature and synthesis: reviewing research on AI harms, help-seeking, gaming-the-system, and educational measurement (including the Standards for Educational and Psychological Testing) to ground design decisions.

Skills required:
We welcome students from computing science, data science, education, psychology, statistics, or related fields. Ideal candidates have some combination of: comfort with data analysis or statistics (R or Python preferred); interest in measurement, psychometrics, or research methods; and curiosity about the ethical dimensions of AI. Experience with qualitative coding, inter-rater reliability, machine learning, or NLP is an asset but not required. Equally important are careful, critical thinking, attention to definitional precision, and strong communication skills. Students who enjoy distinguishing subtle conceptual differences—such as harmful over-reliance versus healthy help-seeking—will find this project especially rewarding.

Research area: Microsystems, VLSI, Thermal dynamics, Thermal monitoring on microsystem, Thermal diffusion on biological tissues. Technology IP Porting.

Student roles:
- Medical Image Processing (Matlab/OpenCV)
- Feature Extraction algorithms
- Role of the student in the research project mentioned above: Feature extraction using image processing

Skills required:
The objectives of the Projects are
• Perform feature extraction from the thermal images/ videos.
• Find the hottest spot of the body or a specific Region of Interest.
• Compare the Region of interest with the player’s previous thermal images.
• Build the patient thermographic profile of the player during his training and playing matches.
In this project, the student will be involved for Image processing and collecting thermal images of players.

Research area: Deep learning is a subclass of machine learning which has recently produced promising results in many application areas, including computer vision and artificial intelligence. Recently, deep learning approaches have been shown to delineate structures from medical images adequately. Many medical imaging modalities produce a large number of images, and analyzing all these images is tedious and time-consuming. Automating the analysis could lead to a better understanding of the underlying condition. However, automating the analysis using traditional image processing approaches poses many challenges. This project focuses on utilizing deep learning approaches to automate medical image assessment.

Student roles:
The student will be responsible for designing the deep convolutional neural network to perform the cardiac functional analysis. This involves writing codes in the Python programming language. The magnetic resonance imaging datasets and ground truth segmentations will be provided. The student will be encouraged to perform a literature search on the recent developments in machine learning approaches for medical image analysis. The student will also be responsible for comparing the results of the automated method with expert manual segmentation. Guidance will be provided in the design of the network and analysis of the final results. The student will be encouraged to present their results as poster or podium presentations at local (e.g., Mazankowski cardiac sciences research day, radiology research day, etc.) or regional meetings. The student will get an opportunity to work with post-doctoral fellows, graduate students, medical students, and undergraduate students in the lab.

Our lab is located inside the Mazankowski Alberta Heart Institute at the University Alberta Hospital in Edmonton, Alberta, Canada. This offers an opportunity to regularly interact with cardiac radiologists, cardiologists and other medical professionals who will provide feedback on the methodology and results. The student will get an opportunity to discuss their progress in weekly lab meetings (virtual or in-person).

Skills required:
The project requires programming skills in Python. The successful candidate will be an undergraduate student from computer science, mathematics, electrical engineering, computer engineering, or any other related discipline with a background in image processing & visualization and computer graphics. Previous coding experience with deep learning frameworks such as Tensorflow or PyTorch and image processing software packages such as Insight Segmentation and Registration Toolkit (ITK) or OpenCV is preferable. Any prior experience with publicly available tools such as 3DSlicer or MONAI would be an asset.

Research area: This research is on data-driven robot learning from demonstrations. The aim is to enable robots to learn not only from successful demonstrations but also from failures and recovery behaviors. Recent advances in imitation learning and meta-learning have improved robots' ability to generalize across related manipulation tasks; however, these methods rely primarily on successful task demonstrations. This research investigates whether failure and recovery experiences can also serve as transferable knowledge. The work contributes to the development of robotic systems capable of adapting to unexpected situations in manufacturing, assembly, and other complex bimanual manipulation environments.

Student roles:
The student will contribute to the development and evaluation of machine-learning methods that enable robots to learn from failures and recover more effectively during manipulation tasks.

The project will begin with a literature review of learning-from-failure, imitation learning, and meta-learning methods used in robotics. The student will become familiar with a recently developed laboratory dataset containing 2,160 demonstrations collected on the ALOHA Stationary robot-learning platform, including both successful demonstrations and expert recovery trajectories across 18 assembly tasks.

The student will assist in analyzing task structures, failure modes, and recovery patterns within the dataset. Using existing software tools and GPU computing resources, the student will help implement benchmark evaluations of existing learning-from-failure and meta-learning algorithms. Experimental results will be analyzed to determine how effectively current methods transfer recovery knowledge across tasks.

Working alongside graduate researchers, the student will then assist in adapting two meta-learning approaches to incorporate failure and recovery experiences during training. Particular emphasis will be placed on identifying shared failure structures across tasks and evaluating whether these structures can improve adaptation to previously unseen manipulation scenarios.

The student will participate in training and evaluation experiments, analyze recovery performance metrics, and investigate factors affecting sample efficiency and task generalization. Results will be validated across the 18 benchmark tasks and compared against existing approaches.
Throughout the internship, the student will gain hands-on experience in robotics, machine learning, large-scale dataset analysis, experimental evaluation, and scientific research. Deliverables will include benchmark results, software implementations, performance analyses, technical reports, and contributions to conference papers, posters, and presentations.

Skills required:
The ideal student is enrolled in computer science, software engineering, electrical engineering, mechatronics engineering, mechanical engineering, or a related discipline. Experience with Python programming is required. Familiarity with machine learning, robotics, computer vision, deep learning frameworks such as PyTorch, or data analysis is considered an asset. Students should possess strong analytical and problem-solving skills, curiosity for artificial intelligence research, and an interest in robotics and autonomous systems.

Research area: Experimental analysis of soft computing techniques, especially metaheuristics for continuous domains (e.g. Particle Swarm Optimization). Application of optimization-based tools for the improvement of machine learning techniques (e.g. neural networks).

Student roles:
- Attend regularly scheduled meetings at least once per week.
- Work cooperatively with other lab members to coordinate operation of experiments, collection of data, and analysis
- Design, develop, and execute experiments to collect data on how performance-based results are achieved
- Present fully verified results suitable for publication

Skills required:
- Advanced programming skills, especially Python.
- Understanding of search, optimization, soft computing, and computational intelligence
- Strong data analysis and data visualization skills
- A background in science (e.g. controlled experiments) will also be useful

Research area: Our lab investigates novel input techniques for wearable computing, with a focus on smartwatches and small-screen devices. We design and evaluate interaction methods that reduce visual attention demands, enabling users to operate wearables during ongoing activities. Our work spans gesture recognition, sensor fusion, and machine learning — using commercially available IMU and capacitive sensors in consumer smartwatches to decode subtle hand and wrist movements. We run user studies to evaluate input accuracy, learnability, and social acceptability, translating findings into practical design guidelines for eyes-free wearable interaction.

Student roles:
The intern will contribute to all phases of the 12-week project. In weeks 1–2, the intern will review related literature on gesture recognition and wearable interaction and set up the development environment. In weeks 3–5, the intern will design the gesture vocabulary, develop a data collection app for the smartwatch, and run data collection sessions with a few participants. In weeks 6–8, the intern will preprocess sensor signals, train and evaluate classifiers using leave-one-out cross-validation, and select the best-performing model. In weeks 9–10, the intern will implement an eyes-free prototype application on the smartwatch integrating the trained classifier. In weeks 11–12, the intern will run a comparative user study against touch input and analyze the results. The intern will meet weekly with the supervisor and is expected to contribute to a conference paper draft targeting CHI or MobileHCI.

Skills required:
Background in computer science or electrical engineering. Proficiency in Python for signal processing and machine learning (scikit-learn, PyTorch, or TensorFlow). Familiarity with Android or WearOS / Swift or watchOS development is a strong asset. Experience with time-series classification and interest in human-computer interaction

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 role of the student would primarily be software development, testing, and documentation. Testing and documentation would involve creation of novel interactive systems using the new features with existing or ad hoc input devices and media synthesizers. Work would be conducted at the Graphics and Experiential Media (GEM) Lab at Dalhousie University. The student would also be expected to attend weekly planning meetings and weekly general lab meetings with the entire research group.

Skills required:
As mentioned above, the specific features chosen for development can be tailored somewhat to the student's areas of expertise. Knowledge and experience in computer networking is advantageous, as is experience with programming in C or C++. The core library we will be working on is written in C, however we also support language bindings for many popular programming languages so knowledge of other language could be an asset.

Research area: My specialized research area is Natural Language Processing (NLP). NLP is one of the most important and useful fields of artificial intelligence dealing with automatic text processing. Its widespread application is due to the ubiquity of language and text as a main human communication channel, e.g., emails, blogs, social media, web search, chatbots, medical reports, etc. The objective of my research is to devise new methods for machine language understanding, which include language representation, summarization, and classification from unstructured and unconventional text streams mainly in an unsupervised fashion.

Student roles:
- Automatic crawling of social media text and collecting relevant data to the Project.
- Creation of large corpora on sensitive content in a social context, thorough the cleaning and processing of collected data, and the development of specific annotation guidelines.
- Explore and synthesize research papers in NLP and AI conferences/journals related to Anti-social text processing as well as practices on datasets creations from social media. Get Familiar with state-of-the-art technology and provide a critical view of it
- Designing algorithms and architecture to
- predict the type and target of provocative posts; and/or
- predict the user's ability to send a deliberately offensive or provocative message; and/or
- detect different kinds of offensive language such as hate speech and cyberbullying; and/or
- detect sensitive content in open domain conversations
- Regularly communicate with, present, and report progress to the supervisor.
- Interact and collaborate with graduate students and other researchers to get introduced to the research environment and practices.

Skills required:
Expertise in any of these fields: Computer Science, Computer Engineering, Information Science and Technologies.
Prior programming experience in Python. Experience with PyTorch or Tensorflow is a plus.
Knowledge of basic multivariate calculus, linear algebra, probability, and statistics.
Familiarity with the essentials of data mining, machine learning, or artificial intelligence.
Being previously introduced or worked on a project involving NLP.

Research area: Despite recent advances improving the usability of Virtual Reality (VR), cybersickness remains a significant barrier to widespread adoption. Symptoms such as eye strain, nausea, and disorientation frequently disrupt the user experience. The leading explanation for this phenomenon is the visuo-vestibular mismatch, which occurs when the visual system perceives self-motion in the virtual environment while the vestibular system (inner ear) senses that the physical body is stationary. While current interventions exist, they are often disruptive (e.g., forcing a restricted field of view) and focus almost exclusively on the sense of vision. Our lab postulates that subtle, consciously imperceptible vibro-acoustic (haptic and

Student roles:
During the internship, the student will integrate into our lab's culture under the direct mentorship of a senior PhD student. Responsibilities include:
- Conducting a targeted literature review on cybersickness and multisensory interventions.
- Implementing subtle haptic and auditory stimuli in Unity using specialized design tools.
- Designing, pilot-testing, and executing a formal user study.
- Performing statistical analysis on collected cybersickness and UX data.
- Assisting in drafting an academic publication.
- Participating in weekly lab meetings to present progress and exchange ideas.

Skills required:
Required: Completed or currently pursuing a degree in Computer Science, Software, Electrical, or Computer Engineering. Proficiency in C# and the Unity game engine. Strong autonomy and problem-solving skills.

Preferred: Experience with haptic effect design tools, spatial audio, or hardware-software integration. Exposure to HCI user studies and UX evaluation methodologies.

Willingness to Learn: Highly motivated to learn statistical data analysis, safely conduct cybersickness protocols, and assist in scientific writing within a collaborative, multidisciplinary lab environment.

Research area: Smart environments combine sensors, embedded devices, AI systems, and spatial interfaces. As spaces become more computationally aware, people need intuitive ways to understand, control, and collaborate with them. Mixed reality and spatial computing can provide a front-end layer for Internet-of-Things systems by anchoring digital information, controls, visualizations, and assistant-like interfaces directly to physical objects and locations. This research area explores mixed reality interfaces for smart environments, including 3D interaction, contextual sensing, sensor visualization, spatial user interfaces, and human-centered interaction design.

Student roles:
The student will help design and develop a mixed reality interface for a smart-environment or IoT scenario. The student will review relevant tools and frameworks, help define a focused prototype use case, and implement a spatial interface using an appropriate platform such as Unity, WebXR, ARKit, ARCore, OpenXR, or another suitable development environment.

The prototype may involve anchoring digital controls, visualizations, or assistant-like interface elements to physical devices or locations in an indoor environment. The student may connect the interface to live or simulated sensor data, environmental devices, or simple networked objects. Depending on the student’s skills and available equipment, the project may use a mixed reality headset, mobile AR device, desktop simulation, or web-based 3D environment.

The student will document design decisions, compare technical approaches, and, where feasible, conduct a small-scale evaluation of the prototype with users or lab participants. Evaluation may focus on usability, clarity of spatial information, usefulness of the interface, or user experience. The student will work with the research team on prototype development, testing, refinement, and possible dissemination through demos, posters, workshops, technical reports, or publications.

Skills required:
An optimal skillset includes a broad background in computer science, interaction design, digital media, or a related field; programming experience in C#, JavaScript, Python, Java, C/C++, or similar languages; interest in mixed reality, augmented reality, virtual reality, 3D graphics, or game development; familiarity with Unity, Unreal Engine, WebXR, OpenXR, ARKit, ARCore, Vuforia, Three.js, or OpenGL; and basic understanding of IoT or maker technologies such as Raspberry Pi, Arduino, sensors, microcontrollers, or wireless networking.

Research area: Our research group is generally interested in designing and evaluating applications - mobile app, web app, games, virtual reality. These are active Human-Computer Interaction (HCI) research areas. We are mostly interested in using these systems to solve problems that are of global importance especially in the area of health and wellness (e.g, promoting mental health, healthy lifestyle, physical activity, healthy eating, smoking cessation) and safety and security (e.g., avoiding risky driving behaviour). Problem domains such as energy conservation and climate change are also of interest.

Student roles:
The student will join a team of faculty, postdoctoral researchers and graduate students working on various projects at the Human-Computer Interaction Lab. The student would work with graduate students on projects that involve developing and evaluating persuasive systems for promoting mental health. There is flexibility for the students to focus on a particular application; games, mobile application, social media, and web application design or some combinations. The student would have access to collections of computer science papers and other relevant literature and resources (software and hardware) needed for the project. We hope to publish at least a fully refereed paper at the end of the project and with additional work to be carried out remotely after the end of the Globalink visit we could possibly generate more publications.

Skills required:
The student needs to have good programming and design skills, be interested in working with people. Previous experience with developing mobile applications, social media, web application, and games, design and development will be of advantage. Familiarity with or interest in pursuing graduate studies in one of the following areas would be highly desirable: human-computer interaction, web intelligence, computer graphics, visualization, data analytics, machine learning, text mining, information retrieval.

Research area: Our research group is generally interested in designing and evaluating interactive systems such as Web Applications and Mobile applications for promoting health and wellness. These are active Human-Computer Interaction (HCI) research areas. We are mostly interested in using these systems to solve problems that are of global importance especially in the area of health and wellness (e.g, promoting a healthy lifestyle, recovery, medication adherence, and disease management) and safety.

Student roles:
The student will join a team of faculty, postdoctoral researchers and graduate students working on various projects at the Human-Computer Interaction Lab. The student would work with graduate students on projects that involve developing and evaluating interactive systems. There is flexibility for the students to focus on a particular application; web application, mobile application, social computing, games, or some combinations. The student would have access to collections of computer science papers and other relevant literature and resources (software and hardware) needed for the project. We hope to publish at least a fully refereed paper at the end of the project and with additional work to be carried out remotely after the end of the Globalink visit we could possibly generate more publications.

Skills required:
The student needs to have some design skills, be interested in programming or learning programming. Previous experience with web and mobile application design and development will be of advantage. Familiarity with or interest in pursuing graduate studies in one of the following areas would be highly desirable: human-computer interaction, mobile computing, social computing, game design, visualization, data analytics, machine learning, text mining, information retrieval.

Research area: Software development Mobile phone application Artificial Intelligence Internet of Things Apple iOS, Andoid Big data Machine learning

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 machine learning

Research area: My specialized research area focuses on the security, privacy, and adaptive resilience of safety-critical vehicular communication systems, particularly Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X) networks. My work integrates context-aware communication modeling, misbehavior detection, probabilistic threat analysis, and adaptive protocol design to develop real-time, secure, and privacy-preserving vehicular communication frameworks.

Student roles:
The student will contribute to the design, implementation, and analysis of vehicular mobility simulation scenarios within the VEINS framework. Responsibilities include generating diverse traffic environments, collecting and processing mobility and communication data, supporting the evaluation of safety message behavior through basic plausibility and context analysis, and documenting findings through technical reports and presentations. Students will work collaboratively within a research team, participate in regular project meetings, and contribute to the development of reproducible research artifacts that support broader vehicular communication and cybersecurity research objectives.

Skills required:
Students should have a background in computer science, software engineering, computer engineering, or a related technical discipline. Preferred skills include basic programming experience in Python or C++, familiarity with data analysis, and an interest in networking, cybersecurity, or intelligent transportation systems. Prior exposure to simulation tools, machine learning, or Linux environments would be beneficial, but is not required. Successful students should demonstrate strong analytical thinking, problem-solving abilities, and a willingness to learn simulation tools. The project is well-suited for senior undergraduate students interested in research experience in vehicular networks, cybersecurity, or simulation-based system analysis.

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 area focuses on development and use of current technologies with applications to the scholarship of teaching and learning, sustainable development, problem solving, and intelligent systems using specialized algorithms for the purposes of automated control and artificial intelligence.

Student roles:
Working closely with the mentor, the student will first gain experience using the tools (ex. Blender, Unity) for the project while at the same time performing a literature review on body pose estimation and human system modeling. They will then develop the models of the human systems with the rigging points in Blender. Once complete, code to connect the models with the existing body-pose models will be performed and a scene created to view the results. Once complete the system will be tested and results documented.

Skills required:
This project is well suited for students from computer science and engineering disciplines. Students should have a strong problem solving skill set and experience using procedural and object oriented programming languages. Students with experience in a AR and/or VR as well as experience using Blender and Unity software will be considered an asset.

Research area: - Statistical signal processing - Probabilistic modelling and Bayesian inference

Student roles:
The student will participate in the development, testing, and analysis of sport rating algorithms using both synthetic and real-world match data. They can focus on the mathematical/algorithmic side or on practical implementation, depending on their background and interests.

Skills required:
- Background: engineering (electrical/computer), mathematics, or statistics
- Programming: Python (or strong willingness to learn)
- Autonomy: self-directed learning capacity

Research area: I am a marine biologist with a background in applied math, microbiology and oceanography: my research focuses on small-scale interactions taking place between individual plankton (bacteria, algae, zooplankton/larvae), and how these interactions drive large-scale processes relevant to climate and elemental cycling in the global ocean. To investigate how plankton interact with their physical and chemical environment, we use lab-based microscopy visualization techniques and complementary numerical modelling approaches. This research is particularly urgent as the global ocean undergoes unprecedented physical and chemical changes due to climate change, such as increasing sea surface temperatures and ocean acidification.

Student roles:
The student will work together with the PI and collaborators to formulate a mathematical model of bioconvection in the surface microlayer (SML) which includes fluid flow, carbonate chemistry, and microbial activity. Subsequently, we will work together to develop Python-based simulations solving these equations to make predictions for ocean-atmosphere carbon exchange. The student will lead an exploration of this model system by varying parameters, analyzing output data, creating high-quality visualizations including movies. The student will interact with and work together with a graduate student who is conducting lab studies of bacterial swimming near the air-water interface. At the end of the project, the student will prepare a small report and presentation on the summer research. The student’s work will be incorporated into a larger manuscript on which the student will be a co-author.

Skills required:
Essential skills: Working knowledge of multivariable calculus and differential equations, experience coding in Python, interest in ocean biology and climate science.
Beneficial skills: Coursework in fluid dynamics, numerical methods, and/or ocean science.

Research area: Machine learning and Artificial Intelligence are widely are used in information systems and services such as search engines, online shopping sites, streaming services, and social media. This has raised concerns about their security, safety, and trustworthiness. In this project, we will explore and propose models to protect systems based on Machine learning and Artificial Intelligence from potential threats. We will evaluate the security strengths and weaknesses of the proposed models, in addition to the benefits we can obtain, so that we can trust the results.

Student roles:
Evaluate the State of the Art.
Propose models to improve the security and the trustworthiness of Artificial Intelligence and Machine Learning systems.
Evaluate the Accuracy of the proposed models

Skills required:
Artificial Intelligence and Machine Learning.
Programming.
Data Analysis.
Information security

Research area: My research lies at the intersection of distributed systems and cybersecurity, focusing 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 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, interoperability, and decentralized identity, as foundations for secure and trustworthy decentralized applications.

Student roles:
Intern 1 — Cross-Layer Data Collection & Environment Setup
Design and implement a lightweight framework to collect cross-layer messaging events from L1 and L2 (e.g., via APIs or public nodes)
Develop tools to extract, preprocess, and structure on-chain data, including logs and transaction traces
Implement a message correlation pipeline to link L1–L2 message pairs using identifiers such as hashes or event references
Support dataset generation for normal and anomalous message behaviors
Intern 2 — Analysis & Detection Framework
Design and implement the analysis and detection module for identifying inconsistencies in cross-layer messaging
Develop methods to detect anomalies such as replayed messages, delays, ordering issues, and abnormal patterns
Define and compute evaluation metrics (e.g., latency, finality time, detection accuracy, false positives)
Analyze results and characterize system behavior under different conditions

Skills required:
Candidates should have a solid foundation in computer science, particularly in distributed systems, computer networks, and cybersecurity, along with understanding of blockchain fundamentals, including Layer 2 architectures (e.g., rollups) and cross-chain or cross-layer communication. Proficiency in Python (and optionally languages such as Go or Solidity) is expected for implementing prototypes and data analysis tools. Familiarity with on-chain data access (e.g., interacting with blockchain nodes, APIs, or event logs) and experience with data processing and analysis are important. Exposure to security concepts, including adversarial models and anomaly detection, is an asset.

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, 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 to:
• Set up the system to be used for experimentation.
• Develop a temperature sensor simulator.
• Implement a monitoring system deployed in the cloud.
• Deploy a monitoring system at the edge.
• Evaluate resource consumption at the edge and cloud nodes, and assess the impact of high-load deployment at the edge.
• Evaluate the effectiveness of various failure recovery mechanisms.
• Compare the experimental results with those reported in the state of the art.

Skills required:
- Mastery of fundamental software engineering concepts
- Mastery of fundamental cloud computing concepts
- Mastery of fundamental IoT systems concepts
- Mastery of fundamental service-oriented architecture concepts
- Proficiency in at least one programming language (Python, Java, C++, C#, etc.)

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 play an active role in designing, coding, and evaluating system components for the project. This includes implementing core modules for indexing, search, and retrieval, as well as developing scripts for running experiments and collecting performance metrics.

A key part of the role is hands-on systems building, where the student will translate high-level ideas into working prototypes. The student will also conduct experimental evaluations, analyze results, and help refine the system based on performance insights.

The student will work closely with graduate students in the lab, participating in regular discussions, receiving mentorship, and contributing to a collaborative research environment. This provides an opportunity to learn research practices, system design thinking, and experimental methodology.

Overall, the role emphasizes practical implementation, system evaluation, and teamwork within a research setting focused on modern AI and database systems.

Skills required:
Students should have a background in computer science or computer engineering. A solid understanding of data structures and algorithms is required, along with familiarity with operating systems concepts. Basic programming experience (e.g., Python, C++, or Java) is expected. Prior exposure to database systems is preferred but not required. Interest in systems building, performance evaluation, and modern AI applications (e.g., LLMs, RAG) is a strong plus.

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 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: This research project investigates the computational analysis and generation of artistic movement, with a focus on conducting gestures and dance. Using machine learning, computer vision, multimodal AI, and motion capture technologies, the project explores how movement communicates rhythm, expression, and artistic intent. Students may contribute to research in music-motion synchronization, gesture recognition, motion generation, and interactive performance systems. The project is part of the interdisciplinary MovementAndMeaning (MAM) initiative, bringing together computing science, music, dance, theatre, and cognitive science to develop intelligent tools that deepen our understanding of movement, creativity, and human expression.

Student roles:
1. To conduct a thorough review of the existing literature on the subject of using multi-touch interface to notate dance and/or conducting gesture.
2. Identify and implement a sub-set of the functionalities for the iPad platform.
3. To help with evaluation the software with a selected group of users.

Skills required:
- Experience in developing software in C, C++ or Java
- Basic knowledge in software engineering
- Interest in real-world applications of advanced algorithms
- Experience with computer graphics and scientific visualization is a plus

Research area: I conduct research broadly in the field of human-computer interaction (HCI). I am interested in building innovative interactive systems and evaluating the user experience of technology. This covers both technologies that have permeated our daily lives (e.g., smartphones) as well as emerging technologies (e.g., virtual reality). Currently, I am particularly interested in cross-reality---the act of moving between realities, such as between virtual reality and physical reality.

Student roles:
The students will primarily act as developers with components of design and human-centered research included. They will have the following main responsibilities:
1) Build a system that supports cross-reality interaction (e.g., by supporting the users' awareness of multiple realities, by helping them resume their work when returning to the reality, or by helping them transfer their work from one reality to another). Students can focus on a specific problem and solution that the system will support.
2) Design and conduct an evaluation of the system with real users to gather feedback and initial impressions. This includes analyzing and reporting the results. The supervisor will work closely with the students when designing the study.

Skills required:
The students should have a background in computer science, software engineering, human-computer interaction, or similar field. They should have good programming skills, and preferably experience with computer vision and/or sensing technologies. Experience with head-mounted displays, virtual reality, augmented reality, human-centered design, and/or user studies is a significant advantage. The students must have good communication skills and the ability to work in a team.

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 multi-agent reinforcement learning for autonomous driving in dense traffic scenarios. 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 simulator setup to algorithm implementation, experimentation, and result analysis.

In the first phase of the internship (weeks 1–3), the intern will conduct a focused literature review on MARL methods applied to autonomous driving, with particular emphasis on existing benchmarking studies and simulation environments. The intern will set up and configure open-source driving simulators such as SUMO and Highway-Env, familiarize themselves with their APIs, and reproduce results from key baseline driving algorithms to establish a solid foundation for subsequent experimentation.

In the second phase (weeks 4–8), the intern will extend the chosen simulator environments to support many simultaneous agents operating in dense traffic scenarios. This involves implementing a range of standard MARL baselines such as MADDPG and MAPPO, and designing appropriate reward structures that incentivize safe and efficient driving behaviours. The intern will run initial experiments to validate the correctness of implementations and iteratively refine the experimental setup based on results.

In the third phase (weeks 9–12), the intern will conduct a systematic empirical evaluation of all implemented MARL algorithms across multiple dense traffic scenarios. Performance will be assessed using standard driving metrics including collision rate, stability rate, and completion rate, with comparisons drawn against human driving behaviour benchmarks. The intern is expected to produce clear visualizations of results and contribute to a written research report summarizing the methodology, experiments, and conclusions, which may form the basis of a future research publication.

Skills required:
Applicants should have a solid foundation in machine learning, with strong 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, actor-critic methods) through coursework or independent study is highly desirable. Experience with scientific computing libraries such as NumPy and Matplotlib is required. Familiarity with autonomous driving simulators (e.g., SUMO, Highway-Env, CARLA) or robotics is a strong asset. Strong analytical and problem-solving skills are essential, as the project involves implementing, evaluating, and comparing multiple multi-agent reinforcement learning algorithms across complex driving scenarios.

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 primarily contribute to the design and software implementation of the MAS-based API. The project entails developing and evaluating a functional prototype to validate a distributed computing architecture tailored specifically for healthcare IoT environments.
Key responsibilities and expected deliverables include:
• Architecture & API Design: Engineering a scalable, MAS-based API capable of managing distributed nodes and optimizing data routing within a healthcare IoT infrastructure.
• Prototype Implementation: Developing a prototype that optimizes communication overhead and coordinates edge-cloud computation distribution.
• Security & Privacy Protocols: Implementing architectural mechanisms to guarantee secure context interchange and privacy-preserving data workflows aligned with FML principles.
• System Integration: Connecting the newly developed API layer with the AMI-Lab's existing IoT context-awareness components.
Through this role, the student will gain deep technical expertise in Distributed AI, MAS frameworks, and secure IoT infrastructures—highly valued domains in both advanced graduate studies and industrial R&D—while contributing directly to publishable research.

Skills required:
Core Qualifications:
• Research Motivation: High degree of self-motivation to engage with complex, non-trivial research and development challenges.
• Software Engineering: Strong command of software engineering methodologies, clean code practices, and architectural design patterns.
• Programming Proficiency: Advanced programming skills, with recommended proficiency in C++ (or a demonstrated ability to rapidly adapt to complex C++ codebases).
Recommended Assets:
• Experience with Web Services and API paradigms (e.g., REST, WebSockets).
• Foundational knowledge of, or academic interest in, Multi-Agent Systems, Distributed AI, or Federated Learning.

Research area: My research focuses on the development of machine learning, deep learning, and evolutionary optimization techniques for analyzing complex biomedical data. A major area of interest is computational pathology, where artificial intelligence is used to extract clinically meaningful information from digital histopathology images. I also work on multi-modal data integration, combining pathology images with genomic, molecular, and clinical data to improve disease diagnosis, prognosis, and treatment prediction. My research emphasizes feature selection, model interpretability, and efficient machine learning methods for high-dimensional biomedical datasets.

Student roles:
The student will participate in all stages of the research project, including literature review, data collection, preprocessing, feature extraction, and machine learning model development. They will work with multi-modal cancer datasets consisting of histopathology image features, genomic information, and clinical variables. The student will implement and evaluate feature selection algorithms, conduct computational experiments, and analyze the impact of selected features on predictive performance and model interpretability.

The student will be responsible for documenting experimental procedures and results, maintaining reproducible code, and presenting progress during regular research meetings. They will also assist in analyzing findings, preparing technical reports, and contributing to conference papers or journal publications. Through this project, the student will gain hands-on experience in computational pathology, machine learning, biomedical data analysis, and scientific research.

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: I'm an Assistant Professor at the School of Electrical Engineering and Computer Science at Ottawa University since January 2020. My main research interests are focused on Artificial Intelligence, Machine Learning, Knowledge Discovery and Data Mining, and in particular in utility-based learning, imbalanced domain problems, rare events mining including outlier detection, anomaly detection, fraud detection and rare extreme values forecasting. I'm also interested in several applications of my research such as: cybersecurity problems (malware and intrusion detection), health care problems (rare disease detection, cancer prediction), failure and fraud detection or ecological/meteorological problems (forecasting weather extreme events).

Student roles:
During this internship the student will be responsible by the following tasks:
- Understand the problem of semi-supervised learning and active learning strategies;
- Understand the main resampling methods to deal with imbalanced domains and several outlier detection methods;
- Implement in Python an experimental setting to train and test different solutions for the multi-task active learning problem;
- Run the experiments in Python and modify the solutions when necessary to make them more suitable and with increased performance for the problem;
- Write a report that describes the algorithms implemented, and the experiences carried out, and that summarizes the results obtained.

Skills required:
We are looking for a student from the computer science field or related area with knowledge and interest in artificial intelligence and machine learning.
To work in this project the student should have:
- Basic knowledge and experience with machine learning algorithms and performance evaluation methods (cross-validation, bootstrapping, etc)
- Knowledge on supervised, semi-supervised and unsupervised learning tasks
- Experience setting up and running experiments in Python
- Good written and communication skills.

Research area: I do research in natural language processing, which is an area of AI. More specifically, I work on text generation and language reasoning using large language models.

Student roles:
The student will become familiar with the project through literature review, data collection, script development, running experiments, collecting results, and documenting the project.

Skills required:
Natural language processing is concerned with concepts, models, and algorithms for interpreting, generating, and learning natural languages, as well as their applications. The goal for students is to become familiar with the basic concepts in NLP, understand the algorithms and methods used in NLP, and acquire the skills needed to develop NLP tools and systems.
We are looking for interested and qualified students to spend the summer working on ongoing research projects at the University of Lethbridge in natural language processing. Strong programming skills, familiarity with algorithms, and experience in scripting, development, and debugging are required for the project.

Research area: My research interests lie in the intersection of Human-AI Teaming, Visualization, and Human-Computer Interaction.

Student roles:
The student will work on literature review, design exploration, prototype development, and user-centered evaluation design, depending on their background and interests.

Skills required:
1. Strong interest in HCI/UX/Visualization
2. Familiar with data analysis
3. Good communication and interdisciplinary collaboration skills
4. Highly self-motivated

Research area: We bring together psychology, kinesiology, and child and youth studies, while drawing upon motion capture technology and artificial intelligence. Four research labs (Brock Dance Research; Developmental Processes in Health & Well-being; Neuromechanics & Ergonomics; Spine Biomechanics & Neuromuscular Control) are collaborating on this project. We explore how marker and marker-less motion capture pick-up competitive dancers' distinct movements and patterns. We also train an AI system using in-person models and other video input to enhance how AIs can analyze videos of dancer movement and accurately detect body inversions, rotations, and fine, complex movement in the abdominal muscles (core), spine, and torso.

Student roles:
The role of the student will fluctuate with the current priorities of the lab. The primary role of the student will be twofold. First the student will be largely focused on the project of interest as identified in conjunction with the supervisor. In addition to that role, the student will also be involved in supportive activities related to lab priorities opening up opportunities for additional training and publications. The project of interest is designed such that the student can complete the project and move into dissemination within the 12 week window. This may involve assisting on an existing project or creating a new small project. Students will also be offered opportunities to join in on other projects already in process. The supportive lab environments encourages questions and strong engagement. The student will participate in weekly lab meetings as well as weekly one-on-one meetings with the supervisor. Throughout each week the student will have individual activities to be accomplished along side group activities that will involve working with various lab team members as small, focused working groups. Students will also work directly with Dr. Molnar and Dr. Zinga as well as Dr. Beaudette, and Dr. Holmes. Throughout the time in the lab the student will be actively engaged in research training in the following areas: motion capture; AI; ethics; research data management; research methodologies; transcription; data cleaning; interviewing techniques; qualitative and/or quantitative analysis; knowledge mobilization. The student will gain an appreciation of the research life cycle and have the opportunity to put training into practice. The successful student is expected to take an active role in co-designing the training program with the supervisor and identifying key goals to be accomplished during the internship. Lab meetings and supervisory meetings will be used as an opportunity to monitor progress towards goals and timelines.

Skills required:
The successful student will need to be able to work well in a team environment and independently. They will also be able to manage their time effectively across multiple tasks and deadlines. Students should have some knowledge of quantitative and qualitative research methods. Training in Kinesology, movement, AI, and/or motion capture are also an asset. Strong work ethic and openness to training and constructive feedback are essential. Proficiency in literature searches, strong communication skills, strong organizational skills, attention to detail, and ability to learn programs (e.g. NVivo, SPSS) and research procedures are also key skills.

Research area: My research focuses on multilingualism, cognitive reserve, healthy aging, and language assessment. I investigate how language experiences across the lifespan influence cognitive health and resilience to age-related decline. Using interdisciplinary approaches that integrate speech-language pathology, cognitive science, sociolinguistics, and computational linguistics, I develop and validate measures of multilingualism and examine their relationship with cognitive reserve. This work aims to improve understanding of how language diversity contributes to cognitive well-being, active aging, and social participation in increasingly multilingual populations.

Student roles:
Two undergraduate research interns will contribute to the analysis of multilingual language data collected from approximately 80 middle-aged and older multilingual adults. As participant recruitment and data collection have been completed, the students' responsibilities will focus on data processing, transcription, coding, and quantitative analysis.
The first student will primarily support language-focused analyses. Responsibilities will include organizing multilingual language samples, transcribing discourse recordings, coding verbal fluency and discourse measures using established protocols, and conducting quality-control checks. The student will also assist with literature reviews related to multilingualism, discourse analysis, and cognitive reserve.
The second student will primarily support quantitative and computational analyses. Responsibilities will include data cleaning, dataset preparation, descriptive statistics, data visualization, and assisting with analyses using software such as Excel, R, or Python. Depending on experience, the student may contribute to semantic analyses of verbal fluency data and other computational approaches to language research.
Both students will participate in research meetings, contribute to the interpretation and reporting of findings, and assist with knowledge mobilization activities such as conference posters, presentations, or manuscript preparation. Through this internship, students will gain interdisciplinary training in multilingual language research, discourse analysis, data management, quantitative methods, and collaborative research practices within a health and aging research environment.

Skills required:
The project will involve two undergraduate research interns with complementary backgrounds. One student should have training in Speech-Language Pathology, Linguistics, Applied Linguistics, psychology, or a related field, with interest in language sample analysis and discourse research. The second student should have training in Psychology, Cognitive Science, Data Science, Computer Science, Statistics, or a related quantitative discipline. Experience with transcription, coding, data management, or statistical software (Excel, R, Python, or SPSS) is an asset but not required. Both students should possess strong organizational skills, attention to detail, and the ability to work collaboratively in an interdisciplinary research environment.

Research area: The principal investigator's research focuses on computer vision and deep learning, interdisciplinary fields that integrate digital image processing and artificial intelligence. This project seeks to advance multimodal AI perception for situation-aware decision-making in dynamic environments, including autonomous driving and drone-based surveillance applications. The research encompasses domain analysis, multimodal model development, co-learning strategies for cross-modal knowledge acquisition, and edge-oriented optimization techniques to improve computational efficiency and real-time performance.

Student roles:
The intern will play a vital role in this 12-week research project focused on multimodal AI development for object detection, tracking, and situational-aware decision-making.

Key responsibilities include:
(i) Assisting with domain investigation and identifying relevant datasets.
(ii) Planning on an appropriate pipeline for model design, training, and validation processes.
(iii) Implementing and adapting state-of-the-art methods from recent research.
(iv) Proposing and experimenting with novel techniques to improve computational efficiency and model robustness.

The intern will collaborate closely with the research team, participate in regular discussions, and contribute insights to accelerate progress. They will also be responsible for documenting their work, analyzing experimental results, and preparing clear reports summarizing findings. This internship provides a unique opportunity to gain hands-on experience in LiDAR-based
intelligent perception and contribute meaningfully to cutting-edge research with real-world impact.

Skills required:
Ideal applicants are required to have a strong background in computer vision, deep learning, and image/signal processing. Experience in programming languages, such as Python, along with deep learning frameworks, like Keras, TensorFlow, or PyTorch, is essential. Familiarity with Autoencoders, Convolutional Neural Networks, and transfer learning techniques is desired. In general, the candidates are expected to have strong analytical and problem-solving skills, as well as the ability to plan their tasks, conduct experiments, analyze results, organize their findings, and draw meaningful conclusions are also essential for the success of this research project.

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:
L’étudiante ou l’étudiant assumera un rôle central dans le développement, l’optimisation et l’intégration d’une solution d’intelligence artificielle multimodale visant à soutenir le dépistage précoce de profils développementaux associés à l’autisme. La personne travaillera en collaboration avec deux étudiants à la maîtrise qui développent déjà des modèles d’IA pour l’analyse de données audio, vidéo et comportementales. Elle devra s’appuyer sur les modèles existants, comprendre leur fonctionnement, contribuer à leur amélioration et participer à leur adaptation pour une utilisation dans une application mobile et une application Web.
Son rôle comprendra la préparation des données, la structuration des ensembles d’entraînement, de validation et de test, ainsi que la mise en place de protocoles expérimentaux rigoureux pour évaluer les performances des modèles. Elle participera à l’entraînement, au réentraînement, à l’optimisation et à la comparaison de différents modèles, en tenant compte de critères tels que la précision, la robustesse, l’interprétabilité, le coût computationnel et la capacité de généralisation. Une attention particulière sera accordée à la sérialisation, à la compression, à l’optimisation et au déploiement des modèles afin de faciliter leur intégration dans des environnements applicatifs réels.
L’étudiante ou l’étudiant contribuera également au développement de l’architecture logicielle permettant de connecter les modèles d’IA aux interfaces utilisateur. Cela inclura la conception d’API backend, l’intégration des modèles dans une application Web permettant le téléversement de données vocales, ainsi que la contribution à une application mobile destinée à soutenir la collecte et l’analyse de données multimodales. La personne participera aux tests fonctionnels, à la documentation technique, à l’analyse des résultats et à la préparation de rapports ou de publications scientifiques.

Skills required:
Développement mobile, idéalement avec Flutter; développement Web; programmation Python; apprentissage automatique et apprentissage profond; analyse audio et traitement du signal vocal; extraction de caractéristiques à partir de voix et de vidéos; intégration de modèles d’IA dans une application; conception d’API backend, par exemple avec FastAPI; bases de données et gestion sécurisée des données. Des compétences en sérialisation, optimisation et déploiement de modèles d’IA sont également importantes. Une expérience avec PyTorch, scikit-learn, OpenCV ou des bibliothèques d’analyse audio serait cruciale.

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 will begin by assisting with a structured literature review to consolidate recent advances in AI-based fall detection, with emphasis on multimodal approaches involving wearable sensors, environmental sensing, and vision-based systems. This will help refine the problem definition and identify suitable methodologies for implementation. The intern will support the implementation and testing of baseline machine learning models under guidance. This may include simple classification algorithms and introductory deep learning models using established frameworks. The student will also assist in evaluating model performance using standard metrics (e.g., accuracy, sensitivity, specificity) and documenting experimental results.

Skills required:
The ideal student should be currently pursuing an undergraduate degree in Industrial Engineering, Computer Science, or Computer Engineering, or a related discipline. The student is expected to have a basic understanding of programming and data analysis, with proficiency in Python being highly desirable. Familiarity with common programming constructs, data structures, and basic software development practices is sufficient at this stage.

At a minimum, the student should have introductory exposure to concepts in Machine Learning or data analytics, such as classification, regression, and model evaluation.

Research area: My specialized research area focuses on emotionally intelligent, context-aware, and multimodal AI systems for assistive technologies and human-centred computing. My work integrates affective computing, multimodal machine learning, human-computer interaction, and adaptive intelligent systems to support personalized engagement and cognitive interaction in healthcare and assistive environments. Specifically, this project investigates the development of GECR-Benchmark-v1, a curated multimodal benchmark and annotation framework derived from publicly available datasets to support research on emotion-aware, engagement-aware, and context-aware assistive AI systems for dementia-supportive applications.

Student roles:
The student will play an active role in developing GECR-Benchmark-v1, a multimodal benchmark and annotation framework designed to support research on emotionally intelligent and context-aware assistive AI systems. The student will contribute to multiple stages of the research and development process, including dataset curation, multimodal data preprocessing, annotation support, software implementation, and preliminary evaluation of AI models.
The student will begin by reviewing relevant literature in affective computing, multimodal machine learning, human-computer interaction, and assistive AI systems. Following the literature review, the student will assist in identifying and organizing selected public multimodal datasets containing audiovisual, textual, and behavioural interaction data. The student will also contribute to developing annotation schemas for emotional states, engagement levels, contextual interaction patterns, and adaptive interaction indicators relevant to assistive technology environments.
A significant component of the internship will involve technical implementation tasks. The student will support the preprocessing and organization of multimodal data using Python-based tools and machine learning libraries. This may include feature extraction, metadata management, data formatting, annotation integration, and implementation of baseline machine learning pipelines for emotion and engagement analysis. The student may also contribute to developing simple visualization dashboards or interaction analysis tools to support exploratory evaluation.
The student will participate in regular research meetings, collaborate with the research supervisor and project team, and contribute to documentation, technical reporting, and presentation of findings. Through this process, the student will gain hands-on experience in applied AI research, multimodal data analysis, software development, and interdisciplinary research collaboration within healthcare-oriented assistive technology applications.
Overall, the internship will provide the student with practical exposure to research and development activities involving affective computing, human-centred AI, and intelligent assistive systems while supporting the advancement of GECR-Benchmark-v1.

Skills required:
The ideal student for this project should have a background in Software Engineering/Computer Science/Artificial Intelligence/Data Science, or a related field. The student should possess foundational programming skills in Python and familiarity with machine learning and data processing concepts. Experience with AI frameworks such as PyTorch, TensorFlow, or scikit-learn is required.
The project also requires an interest in multimodal data analysis, human-computer interaction, affective computing, or assistive technologies. Familiarity with handling datasets, preprocessing audio/video/text data, and working with basic data annotation workflows would be beneficial.

Research area: My research focuses on developing efficient learning methods that reduce the need for supervision. The objective is to leverage the exponentially growing amount of data generated by the internet and connected systems (e.g., social media, Internet of Things) to build more capable deep learning models while reducing both their computational requirements and dependence on costly human annotations. My work is primarily applied to visual recognition and understanding tasks involving images and videos.

Student roles:
The student will contribute to the development, implementation, and evaluation of algorithms related to the proposed project. To support this work, the student will conduct a literature review of relevant scientific publications to gain a comprehensive understanding of the state of the art in the field.

The student will learn to use and efficiently share the computational resources available in the laboratory, including GPU servers and software infrastructure, in order to perform large-scale experiments. They will be responsible for designing experiments, analyzing results, and assessing the performance of the proposed methods.

The student will also be expected to document their work and disseminate the results through technical reports and, when appropriate, scientific publications.

The student will be integrated into a dynamic research environment and hosted within my laboratory, where they will interact with MSc and PhD students working on related topics in machine learning and computer vision. The student will receive regular supervision from me and will be paired with a more experienced graduate student who will provide day-to-day guidance and technical support throughout the project.

In addition, the student will have the opportunity to participate in the laboratory's regular activities, including reading groups, research seminars, invited talks, and other scientific and social events, providing valuable exposure to current research trends and fostering professional development.

Skills required:
The ideal candidate will have a strong mathematical background, solid programming skills (Python/PyTorch), and prior experience with Deep Learning and Computer Vision. The student should be comfortable developing and implementing algorithms, analyzing experimental results, and collaborating with other students as part of a multidisciplinary research team.

Research area: Natural Language processing / Artificial Intelligence

Student roles:
research, development, state of the art.

Skills required:
good programming skill;
good Machine learning and NLP background.

Research area: My professional goal is to advance the options for personalized treatment of complex medical disorders via application of machine learning and data science. My team focus on developing learning tools to predict diagnostic and prognostic outcomes, in cardiovascular diseases using electronic medical records, electrocardiograms and echocardiograms at the population scale - some of this research has been published in Nature Digital Medicine. In the past, I have developed successful AI methods to identify and predict specific symptom clusters and treatment responses in psychiatric disorders such as Schizophrenia and OCD using multimodal imaging, which have been featured in several news reports.

Student roles:
The student will be primarily responsible for developing and applying multimodal, generative or foundational models for longitudinal patient modeling and prediction of clinically significant outcomes such as rehospitalization, major adverse cardiovascular events (MACE), and mortality, using machine and deep learning libraries in Python. The project will also provide opportunities to investigate mathematically principled approaches for modeling and evaluating longitudinal clinical forecasting systems.

The student will be involved in the some of the following tasks:
Integrate Multimodal Data: Align and harmonize multimodal data, including ECG waveforms, ECHO images and videos, diagnoses, procedures, laboratory measurements, medications, and hospitalization histories.
Investigate Generative Methods: Explore architectures such as VAEs, transformers, and self-supervised learning approaches for learning temporal patient representations and forecasting clinical trajectories.
Train Foundational Models: Develop deep learning models that learn from longitudinal ECG-EHR-ECHO data and encode rich patient-level embeddings.
Develop Forecasting Frameworks: Investigate approaches for modeling longitudinal patient trajectories, recurrent events, and multi-horizon clinical prediction, together with methods for uncertainty quantification and calibration.
Evaluate Performance: Assess models using appropriate forecasting metrics and compare performance against baseline methods and unimodal models, with consideration of clinical utility and robustness.
Compare Modalities: Analyze the contributions of individual modalities and multimodal combinations to predictive performance and interpretability.
Literature Review: Conduct a comprehensive review of generative and foundational modeling approaches, longitudinal prediction methods, and evaluation frameworks for clinical time-series and multimodal learning.

Timeline
Month 1: Define research questions, review relevant literature, prepare and explore datasets, and develop baseline models.
Month 2: Build and validate multimodal generative / foundational models, perform experiments, investigate forecasting and evaluation approaches, and deliver a midway presentation.
Month 3: Refine the modeling pipeline, interpret results, complete the project report, and give the final presentation.

The student is expected to work independently while engaging with a multidisciplinary team, model development, and scientific discussion.

Skills required:
The ideal student should have a strong background in machine learning and deep learning, with experience using Python, PyTorch, or TensorFlow. Familiarity with time-series and vision multimodal modeling, generative models or self-supervised learning is essential. The student should be comfortable handling large-scale, longitudinal data and integrating multimodal inputs. An interest in the mathematical foundations of clinical prediction—including survival analysis, uncertainty quantification, and principled evaluation methods for forecasting systems—is highly desirable. Prior experience working with healthcare or clinical datasets is an asset. A background in computer science, biomedical engineering, clinical informatics, statistics, applied mathematics, or computational health is a plus.

Research area: I am a professor of Computer Science at Ontario Tech University, where I lead the Visual Computing Lab. Our group works at the intersection of computer vision, deep learning, and human-centric AI, with recent interest in generative models for human motion synthesis. We are particularly interested in how rich multimodal inputs, such as natural language, spatial sketches, and object context, can guide the generation of realistic, socially coherent motion for multiple interacting people. More about our research can be found at http://www.vclab.ca.

Student roles:
# Motion Generation Researcher

This student will implement and adapt a diffusion-based motion generation backbone to accept multimodal conditioning inputs. They will design encoders for each modality (text via a pretrained language model, sketches via a lightweight CNN or ViT, objects via a point-cloud or bounding-box encoder), fuse these representations, and train the joint model on publicly available multi-person motion datasets (e.g., CORE4D, InterHuman, BABEL).

# Interaction Constraint and Evaluation Developer:

This student will focus on enforcing physical realism in generated multi-person sequences. Tasks include implementing contact and collision loss terms, building an evaluation harness using standard metrics (FID, diversity, multimodality, success rate for interaction completion), and conducting user studies to assess perceptual quality.

Skills required:
* Strong proficiency in Python and PyTorch.
* Familiarity with generative models, ideally diffusion models or VAEs.
* Background in linear algebra and sequence modeling (transformers, attention mechanisms).
* Exposure to 3D human body representations (SMPL or SMPL-X) is a strong asset.
* Interest in computer animation, embodied AI, or human-robot interaction.

Research area: I am a former professional game developer whose research group explores game design and development, HCI, games user research. We are recruiting undergraduate interns for our upcoming project, codenamed “Legacy Code,” a dystopian, narrative-driven cyber-thriller and educational adventure game. The interdisciplinary team will include game developers & designers, a digital artist, and a user researcher. Through protagonist's journey, the game teaches critical thinking about AI, digital hygiene, and the value of human connection. Players will learn to recognize manipulative digital systems, including infinite scrolling, AI substitutes for relationships, and data monetization, while reflecting on the changing nature of online communities.

Student roles:
Role 1: The Developer & Narrative Designer: This intern handles the code (Unity/Unreal, game loops, Dopamine Meter) and writes the branching dialogue. Because they control the logic, they are perfectly positioned to weave the story directly into the gameplay mechanics (like writing the text for the "prompt injection" puzzles).

Role 2: The Digital Artist & Worldbuilder: This intern creates the visual assets (the CRT monitors of 1999 vs. the sterile 2027 smartphone overlays) and shapes the narrative through the environment. They use environmental storytelling—like background posters, graffiti, or NPC clothing—to tell the story of how the world changed.

Skills required:
Role 1: Developer & Narrative Designer
Technical: Proficiency in Unity (C#) or Unreal (C++), state management (coding the Dopamine Meter), and implementing branching dialogue nodes (e.g., Yarn Spinner).
Narrative: Writing concise, interactive scripts and designing text-based puzzles like "prompt injection."

Role 2: Digital Artist & Worldbuilder
Visuals: 2D/3D asset creation (Blender/Photoshop) and post-processing (retro 1999 CRT scanlines vs. sterile 2027 lighting).
UI/UX: Crafting two contrasting interfaces: a clunky 90s OS and a hyper-polished, notification-heavy smartphone.
Environment: Conveying digital addiction through background details and level design.

Interest in sci-fi dystopia is a great asset.

Research area: The Middleware Systems Research Group, led by Prof. Hans-Arno Jacobsen, studies scalable, reliable, and secure distributed applications. The group advances data management and distributed systems research, including middleware platforms for large-scale data processing, real-time event streaming, publish/subscribe systems, content-based routing, and efficient data dissemination across distributed applications. Its cloud-computing research optimizes resource allocation and system performance for data-intensive workloads. This project fits the group's expertise by combining database systems, cloud-native observability, service and infrastructure management, and natural-language interfaces for querying complex distributed systems.

Student roles:
At the start of the program, the student will review literature on root cause analysis, observability for microservices, natural-language interfaces to databases, text-to-SQL, semantic parsing, schema linking, provenance, and query validation. They will also become familiar with the group's experimental environment, including representative cloud-native applications, telemetry collection tools, analytical data stores, and failure-injection workflows.

The student will then design and implement a prototype natural-language query interface for observability data. The system will accept incident descriptions, identify relevant services, time windows, telemetry modalities, and schema elements, and generate executable query plans against observability data stores or container-management APIs. Depending on the available source, these plans may include SQL, metric queries, log filters, trace queries, or Kubernetes inspection commands. The system should retrieve evidence such as abnormal metrics, error logs, slow traces, deployment events, restart information, and dependency failures, while preserving links from derived results back to source records.

The student will evaluate the prototype using controlled incident scenarios in microservice workloads. Example scenarios may include service latency spikes, downstream dependency failures, pod restarts, resource saturation, deployment regressions, API timeouts, and malformed service responses. The evaluation will compare generated query plans with manually written queries, dashboard-style retrieval, and generic LLM prompts over raw telemetry excerpts. Metrics may include query executability, query correctness, evidence precision, root-cause localization accuracy, runtime, number of interaction turns, and reproducibility.

Throughout the project, the student will maintain a clean codebase, document experimental procedures, prepare figures and case studies, and present weekly progress updates. By the end of the internship, the expected deliverables are a literature summary, a working query-interface prototype, a small benchmark of natural-language RCA questions and diagnostic scenarios, reproducible experimental results, and a short workshop-paper draft for a database or data-management venue, with potential extension into a full research paper.

Skills required:
The student should be comfortable programming in Python and using common data-analysis libraries such as pandas and Matplotlib. Familiarity with SQL, databases, REST APIs, Git, and basic Linux command-line usage is expected. Prior exposure to cloud-native tools such as Docker, Kubernetes, OpenTelemetry, Prometheus, Grafana, ClickHouse, or log-analysis systems would be an asset but is not required. Experience with large language models or text-to-SQL systems would also be helpful. The successful candidate should be motivated to read research papers, build prototypes, run experiments, document results, and communicate progress clearly.

Research area: Our research focuses on studying biological/medical networks/graphs and analyzing their structural and behavioral properties to extract novel information. In this line of research, we develop methodologies to study multi-drug therapies and the potential adverse reactions of drug-drug interactions. Applying network science techniques and artificial intelligence algorithms on complex networks of drugs and proteins, we can find drug interactions more efficiently. This will speed up the treatment process, reduce hospital stays and will make our health care system stronger.

Student roles:
- Extract, clean, and preprocess adverse event reports from the FDA Adverse Event Reporting System (FAERS) and the Canadian Adverse Reaction Database (CAERS).
- Develop data integration pipelines to harmonize drug names, adverse event terms, and patient-related information across multiple data sources.
- Construct and maintain a unified adverse event dataset for downstream analysis.
- Perform exploratory data analysis and generate descriptive statistics to identify reporting trends, drug usage patterns, and adverse event distributions.
- Build drug-event and drug-drug interaction networks from the integrated dataset.
- Implement and evaluate graph-based machine learning and Graph Neural Network (GNN) models for adverse event prediction and analysis.
- Compare the performance of GNN models with traditional machine learning approaches.
- Document data processing workflows, experimental procedures, and research findings.
- Contribute to research reports, presentations, and potential academic publications.
- Participate in regular meetings with the research team and communicate progress, challenges, and results.

Skills required:
- Undergraduate student in Computer Science, Data Science, Bioinformatics, or a related discipline.
- Basic programming skills, preferably in Python.
- Familiarity with data structures and database concepts.
- Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib) is an asset.
- Basic understanding of machine learning concepts.
- Interest in artificial intelligence, health informatics, network science, or biomedical data analysis.
- Experience with graph-based methods, deep learning, or Graph Neural Networks is beneficial but not required.
- Ability to work independently while collaborating effectively with the team
- Enthusiasm for learning new computational methods and conducting research

Research area: Social behaviours are important for the survival of species across the animal kingdom. In mammalian species such as rodents, multimodal information is used to guide social behaviours. How social information is processed in the brain has attracted considerable attention over the past decade. In my laboratory, we use in vivo recording techniques to examine the cellular and network mechanisms underlying the processing of social information in rodents.

Student roles:
Students will be responsible for analyzing data from calcium imaging experiments examining social fear memory formation in male and female mice. They will be trained to use established pipelines for the segmentation of calcium imaging data from hippocampal recordings, as well as for behavioural tagging. In addition, they will work with graduate students and postdoctoral fellows in the laboratory to apply computational algorithms to examine the decoding properties of hippocampal population activity.

Students will receive the necessary laboratory and safety training. They will also participate in regular laboratory meetings to discuss recent findings from other groups and to present their own results.

Skills required:
Students should have a background in Python and MATLAB. Some background in neuroscience and biology would also be helpful.

Research area: Analysis of neuronal firing data and imaging/local field potentials; analysis of fMRI, EEG and MEG data; development of statistical methods; machine learning and neuro-AI; analysis of behavior; eye-tracking and analysis of gaze data; quantitative neuroscience; computational neuroscience; modeling.

Student roles:
The student will take the lead in proposing/developing/extending a project (from the state in which it is in, in Summer 2027), and will work in close collaboration with the mentor.

Skills required:
The student needs to be sufficiently fluent in programming (in any language, including Matlab/Python) and feel comfortable with statistical modeling and AI toolboxes.

Prior knowledge of our specific research areas is not a requirement, as long as the student is willing to learn as needed.

Research area: The sciNeurotech Lab investigates how principles from neuroscience — such as how the brain learns, adapts, and maintains efficiency — can inspire new machine learning methods. This NeuroAI research program bridges computational neuroscience and AI, aiming to design systems that are more efficient, robust, and generalizable by drawing on the brain's own computational strategies. We combine theoretical modelling, algorithmic development, and empirical benchmarking on standard AI tasks.

Student roles:
The intern will be an active member of the NeuroAI research team for the duration of the 12-week internship. Their responsibilities will include:

- Literature review: survey recent publications at the intersection of neuroscience and machine learning to identify relevant biological mechanisms and their computational analogs.
- Algorithm implementation: translate neuroscience-inspired ideas into modular, well-tested Python/PyTorch code. This includes implementing learning rules, architectural components, or training procedures motivated by brain mechanisms.
- Experimental design and execution: design controlled experiments with appropriate baselines and ablations. Run experiments on local GPU workstations and/or the Narval HPC cluster (Compute Canada).
- Data analysis and visualization: analyze experimental results using statistical methods, produce clear figures and tables suitable for publication.
- Scientific communication: present progress in weekly lab meetings, write a short technical report summarizing findings, and potentially contribute to a conference paper submission.

The intern will meet regularly (1-2/week) with the supervisor and will participate in the lab's weekly group meetings and journal clubs. The work environment is collaborative, with opportunities to interact with graduate students working on both neurotechnology and computational projects.

By the end of the internship, the intern will have:
- Gained hands-on experience with brain-inspired ML research methodology
- Developed skills in HPC-based scientific computing
- Produced reproducible experimental results with potential for publication
- Built a deeper understanding of how neuroscience can inform AI design

Skills required:
This project is open to students in computer science, engineering, applied mathematics, or computational neuroscience.

Required: strong Python programming skills and familiarity with at least one deep learning framework (PyTorch preferred). Experience with running experiments on Linux/HPC environments is an asset.

Desirable: coursework or research experience in machine learning, computational neuroscience, or optimization. Familiarity with scientific computing, version control (Git), and data visualization.

The candidate should be self-motivated, comfortable reading scientific papers, and interested in the intersection of neuroscience and AI.

Research area: Our research advances neuromorphic computing and its applications in edge AI and digital health technologies. We develop custom brain-inspired computational models alongside ultra-low-power neuromorphic hardware architectures. By tightly coupling these hardware-software systems, our goal is to enable real-time, on-device biosignal processing directly at the edge.

Student roles:
During this 12-week internship, the student will bridge software simulation with edge hardware deployment, taking ownership of the implementation and optimization of our neuromorphic multi-sensor fusion models on physical microcontrollers.

Key Responsibilities:
- Model Translation & Optimization: Adapt pre-trained brain-inspired or spiking neural network (SNN) multi-sensor fusion models from Python environments into highly optimized, deployment-ready C/C++ code or HDL code.
- Bare-Metal Firmware Development: Write structured, low-overhead bare-metal firmware or HDL code to execute the algorithmic pipelines directly on low-power microcontrollers (MCUs) or FPGAs without relying on thick operating system layers.
- Sensor Interfacing & Data Ingestion: Develop driver software to handle real-time data ingestion and synchronization of high-frequency EMG signals and IMU data streams via standard serial protocols (e.g., SPI or I2C).
- On-Chip Benchmarking: Deploy the bare-metal code onto target hardware dev boards and benchmark on-chip performance metrics, specifically analyzing execution latency, fusion classification accuracy, memory footprint, and power consumption.
- Hardware Troubleshooting & Documentation: Work alongside laboratory mentors to debug embedded code using standard hardware debugging interfaces and compile comprehensive documentation, including clean GitHub repositories and a final technical report.

Through this hands-on role, the student will gain critical, real-world experience in embedded systems, edge AI, and hardware-software co-design within an interdisciplinary engineering environment.

Skills required:
Candidates should be upper-year undergraduate students in Computer Science, Computer Engineering, Biomedical Engineering, Electrical Engineering or an equivalent discipline.
- Strong proficiency in bare-metal programming (C/C++) of low-power microcontrollers or HDL coding (SystemVerilog) for FPGAs.
- Strong proficiency in Python (NumPy).
- A solid understanding of digital signal processing (DSP) for time-series data or multi-sensor fusion techniques is essential.
- Prior exposure to neuromorphic computing, spiking neural networks (SNNs), or event-driven programming is highly desirable.

Research area: Our research advances neuromorphic computing and its applications in edge AI and digital health technologies. We develop custom brain-inspired computational models alongside ultra-low-power neuromorphic hardware architectures. By tightly coupling these hardware-software systems, our goal is to enable real-time, on-device biosignal processing directly at the edge.

Student roles:
During this 12-week internship, the student will bridge software simulation with edge hardware deployment, taking ownership of the implementation and optimization of our neuromorphic PPG tracking models on physical microcontrollers.

Key Responsibilities:
- Model Translation & Optimization: Adapt pre-trained brain-inspired or spiking neural network (SNN) models from Python environments into highly optimized, deployment-ready C/C++ code for MCUs or HDL code for FPGAs.
- Bare-Metal Firmware Development: Write structured, low-overhead bare-metal firmware or HDL code to execute the algorithmic pipelines directly on low-power microcontrollers (MCUs) or FPGAs without relying on thick operating system layers.
- Sensor Interfacing & Data Ingestion: Develop driver software to handle real-time data ingestion of 1D optical PPG signals via standard serial protocols (e.g., I2C or SPI), ensuring the microchip can process optical data streams efficiently.
- On-Chip Benchmarking: Deploy the bare-metal code onto target hardware dev boards and benchmark on-chip performance metrics, specifically analyzing execution latency, tracking accuracy, memory footprint, and power consumption.
- Hardware Troubleshooting & Documentation: Work alongside laboratory mentors to debug embedded code using standard hardware debugging interfaces and compile comprehensive documentation, including clean GitHub repositories and a final technical report.

Through this hands-on role, the student will gain critical, real-world experience in embedded systems, edge AI, and hardware-software co-design within an interdisciplinary engineering environment.

Skills required:
Candidates should be upper-year undergraduate students in Computer Science, Computer Engineering, Biomedical Engineering, Electrical Engineering or an equivalent discipline.
- Strong proficiency in Python (NumPy).
- Strong proficiency in bare-metal programming (C/C++) of low-power microcontrollers or HDL coding (SystemVerilog) for FPGAs
- A solid understanding of digital signal processing (DSP) for 1D time-series data or optical biosignals is essential.
- Prior exposure to neuromorphic computing, spiking neural networks (SNNs), or event-driven programming is highly desirable.

Research area: Dr. Stuerzlinger is a leading researcher in virtual, augmented & extended reality (VR/AR/XR), and human-computer interaction. 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. 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 will be performed in collaboration with other students and/or postdoctoral researchers in the lab. Students working on this project will implement a prototype of one or more novel 3D interaction method(s) for Extended Reality systems. Students will also perform a user study to evaluate the usability and performance of the prototype. Finally, students will be involved in the dissemination of the results through co-authorship in scientific publications. Projects will involve the latest Extended Reality systems, such as the Samsung Galaxy XR and Apple Vision Pro.

Skills required:
Required:
- Excellent coding skills
- 3D Computer Graphics course or equivalent completed successfully
- Very good English language skills (speaking, reading, and writing)

Desired:
​- Unity3D skills
- C++, C#, Swift, or Objective-C
- Virtual/Augmented/Extended Reality course or equivalent
- Human-Computer Interaction course or equivalent
- Good presentation skills

Research area: My specialized research area spans numerical analysis, scientific computing, and high-performance computing, with a focus on algorithms and software for AI-enabled scientific computing, emerging heterogeneous computing platforms, and the actor model of concurrent computation as a new paradigm for large-scale scientific simulation. Current projects include real-time simulation of cardiac electrical activity, molecular dynamics for drug design, large-scale hydrological flow simulation, and plasma simulation. The goal is to help drive advances in the computational methods and software behind major societal challenges, including personalized medicine, water security, flood prediction and management, and clean energy, particularly fusion.

Student roles:
The role of the student will be to contribute meaningfully to the team effort to
develop advanced algorithms and software for complex simulations with potential
impact in both research and industry. This effort will involve implementing and
running computer software, processing and analyzing results, and presenting
findings in oral and written form. Depending on the specific project, the
student may help design computational experiments, compare numerical approaches,
assess performance and accuracy, and contribute to the development and testing
of new algorithmic ideas and software.

It will be important for the student to engage with the current research
literature to understand the state of the art and position the work for maximum
impact. The student will be encouraged not only to carry out assigned tasks but
also to think critically, ask questions, identify promising directions, and help
shape the direction of the research as the work evolves. Because some aspects of
this work, particularly those involving actor-based scientific computing, remain
relatively unexplored, the project offers unusual scope for creative thinking,
initiative, and foundational contributions in an emerging research area. These
aspects may include building software or workflows to be used by other partners
beyond the immediate project. Because this is a highly multidisciplinary
project, clear communication and collaboration with team members will be
essential. The student will be expected to communicate effectively, both orally
and in writing, with collaborators in person and through remote and electronic
communication tools.

Overall, the role is intended to provide substantive experience in original
research, interdisciplinary teamwork, and the development of computational
methods and software with the potential for lasting scientific and societal
impact. For a strong student, this project offers the opportunity to make
significant contributions to research outcomes, software, presentations, and
publications, while helping advance new computing paradigms for scientifically
and societally important problems.

Skills required:
The ideal student will have a solid background in applied mathematics, computer
science, or a related area, with some exposure to numerical analysis, scientific
computing, and computer programming. Experience with Python, C/C++, Unix/Linux,
shell scripting, and AI tools to support programming and research communication
would be valuable. Depending on the project direction, familiarity with
high-performance, parallel, distributed, or machine learning methods, the actor
model of concurrent computation, and some physics, chemistry, or
electrophysiology, would be beneficial. Because this is a highly
multidisciplinary project, strong communication, teamwork, and problem-solving
skills are as important as analytical and technical ability.

Research area: Tianzheng Wang's research centres around the making of data-intensive systems that are fast, cost-effective and energy-efficient, in the context of persistent memory, massively parallel processors and next-generation networks. I also often step into related areas such as parallel programming and operating systems while solving problems in database systems. I also have interests in embedded and mobile systems. My recent focus is on concurrency, indexing and parallel programming on persistent memory. Visit https://www.cs.sfu.ca/~tzwang for more details.

Student roles:
- Conduct literature review in related areas
- Work with supervisor and graduate students to devise new system designs
- Carry out performance evaluation and analysis
- Participate in writing necessary reports and papers reporting the result of the project.

Skills required:
- Solid C/C++ programming skills.
- Experience in multi-threaded, concurrent programming.
- Knowledge in characteristics of modern hardware features, e.g., multicore processors, multi-socket (NUMA) architecture, modern storage devices (e.g., PCIe SSDs, persistent memory).
- Related core courses, e.g., database systems, systems programming, distributed systems, operating systems, computer architecture/organization.
- Relevant experience working in areas such as operating systems, database systems and distributed systems not mandatory but a plus.

Research area: My specialized research area lies at the intersection of statistical modelling, artificial intelligence, and computational neuroscience, with a focus on neurodegenerative disorders, including Alzheimer’s disease. I develop data-driven and physics-informed models to study how pathological processes, such as protein aggregation and neuronal dysfunction, propagate across large-scale brain networks. My work integrates multimodal data sources, including neuroimaging and clinical datasets, with advanced methods such as stochastic modelling, Bayesian inference, and machine learning to capture both the dynamics and uncertainty of disease progression. The overarching goal of my research is to build interpretable, multiscale computational frameworks that improve our understanding of

Student roles:
The student will play an active role in the development and implementation of the project’s computational framework. Their responsibilities will include data preprocessing and integration of neuroimaging and clinical datasets, as well as assisting in the design and implementation of machine learning and statistical models. The student will support the development of simulation and network-based models to study disease progression and contribute to parameter estimation using data-driven techniques. They will also be involved in analyzing model outputs, performing validation, and generating visualizations to interpret results. Regular participation in meetings, literature review, and documentation of progress will be expected. The student will collaborate with the research team while also working independently on assigned tasks, contributing to reports and potentially co-authoring research publications.

Skills required:
The ideal student should have a strong background in mathematics, statistics, computer science, or a related field. They should be familiar with probability, linear algebra, and statistical modelling, along with some exposure to machine learning or data analysis. Programming experience in Python, R, or MATLAB is required, including working with scientific computing tools and datasets. Knowledge of stochastic processes, optimization, or network/graph methods is an asset. The student should demonstrate strong analytical and problem-solving skills, be able to work independently, and show interest in interdisciplinary research combining AI, computational modelling, and neuroscience, with a willingness to learn advanced techniques.

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:
- build a dataset of code repositories hosted on niche platforms
- build graphs of contributors to these repositories
- analyze the graphs
- perform interviews with the contributors
- document the methodology and results in a scientific report

Skills required:
Code mining, code analysis, text processing, data analysis

Research area: Dr. Douplik is a Professor, Head of the Photonics Group in the Physics Department at Toronto Metropolitan (formerly Ryerson) University since 2011, a Scientist at St. Michael’s Hospital, Toronto, specializing in Biophotonics and Laser Medicine, author of more than 70 peer-reviewed papers, 55 conference papers, seven book chapters and 18 patents. He was a visiting researcher at the Okinawa Institute of Science and Technology (OIST), Japan, in 2017, 2018 and 2023. From 2007-2011 he was a contract Research Professor at Erlangen University, Germany, where he defended his Biomedical Engineering Habilitation (Dr. of Science degree) in 2011.

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

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

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:
The student will support the development of the non‑invasive hypoglycemia detection and fall‑prevention system by assisting with sensor integration and hardware setup. They will participate in data collection and signal preprocessing to prepare physiological datasets for analysis. The student will help train and evaluate machine‑learning models for early hypoglycemia detection and contribute to deploying these models on embedded hardware using edge‑AI techniques.

They will also assist in developing the IMU‑based fall‑detection module and support prototyping of the airbag‑based fall‑prevention mechanism. Additional responsibilities include contributing to the caregiver alerting and location‑sharing system and ensuring that emergency notifications function reliably.

Throughout the project, the student will maintain documentation, contribute to testing and validation, and assist in preparing final deliverables.

Skills required:
The student should have foundational experience in machine learning and Python programming for handling physiological data. Basic skills in microcontroller programming (ESP32/Arduino) and sensor integration are required to support hardware development. Familiarity with time‑series signal processing and IoT communication will help with real‑time monitoring and alerting. Strong documentation and communication abilities are expected for reporting and presenting project outcomes.

Research area: My research focuses on designing and evaluating intuitive, real-time, interfaces for personal health data exploration on mobile and wearable devices. We develop and study novel interaction techniques and visualization methods that support on-the-go querying and proactive sense-making of data. Moreover, we explore limits to this exploratory capability while on-the-go. Finally, my research also aims to understand underlying dimensions of personal health data which impact perception and understanding of said data. Overall, we aim to empower users to better understand and manage their health data in everyday contexts for increasingly positive and more personalized health outcomes.

Student roles:
The student will play a central role in the design and implementation of a novel gesture-based pointing and selection technique for smartwatch interaction. They will develop a system that enables users to control an on-screen cursor through a pinch-and-move gesture, allowing them to interact with smartwatch interfaces without relying solely on touch input.

Responsibilities will include designing and implementing the software required to detect and interpret pinch gestures, track hand movement, and translate physical motion into cursor movement on the smartwatch display. The student will explore alternative control mappings, cursor behaviours, and selection mechanisms to determine how best to support accurate and efficient target acquisition on a small screen. Particular attention will be paid to challenges associated with wearable interaction, including limited display space, user mobility, comfort, and interaction while walking or exercising.

The student will also contribute to the development of experimental applications that demonstrate how the interaction technique can support common smartwatch tasks, including button selection and exploration of personal health information. This work will involve integrating gesture input with custom smartwatch interfaces/screens while also implementing visual feedback mechanisms. A significant component of the project will focus on preparing the system for future user evaluations. This may include creating calibration procedures, implementing performance measures, and designing software to record interaction events and movement data.

Skills required:
Students should have an interest in software development, human-computer interaction, AND/OR wearable computing. Applicants should have experience developing software through coursework, research projects, internships, or personal projects. Experience with mobile application development (e.g., Swift, Java, Kotlin, React Native, Flutter, or similar technologies) is desirable, although students with equivalent software development experience in other domains are encouraged to apply. Experience with user-centered design, usability testing, or research methods is valuable but not required, as these skills can be developed throughout the project and are secondary to the implementation. Most importantly, students should be motivated to design and build interactive technologies.

Research area: Virtual Reality (VR) omnidirectional treadmills (ODTs) mimic natural walking in virtual environments (VE) by enabling stationary navigation that simulates the sensation of walking. VR-ODTs allow users to explore large VEs beyond the dimensions of the physical room in a safe way, without fear of crashing into walls or colliding with other physical objects. However, using VR-ODT results in unnatural walking, with movement patterns that can diminish immersion and increase discomfort. This has prevented the popularization of VR-ODT, especially in applications where realistic biomechanical movements are required, such as rehabilitation.

Student roles:
The student will take a leading role in the project from conception to completion, under the supervision of the Vertex lab PI. Through this role, the student will develop expertise in immersive systems development, locomotion interface design, experimental methods, and human-computer interaction research. Responsibilities include designing and developing the new gait model, implementing and integrating it in Unity and C# for compatibility with the Cyberith Virtualizer ELITE 2 and Meta Quest Pro, and conducting a user study to evaluate system performance and user experience. For more details, see below:

1. Gait Model Design: The student will identify related work for natural walking methods in VR via a literature review. Topics explored include navigation interfaces, locomotion techniques, gait models, and the impact of eye gaze on locomotion. Using this past work, the student will design a novel gait model.

2. Prototype implementation for the Cyberith Virtualizer ELITE 2: The student will design a VR rehabilitation system that uses the Cyberith Virtualizer ELITE 2 and a Quest Pro. The goal will be for the patient to explore a forest or a beach while walking and trying to find specific elements, e.g., stones, bugs, shells, etc. As part of the prototype, the student will implement the gait model for locomotion.

3. Laboratory study: the student will evaluate the prototype in a user study. The study will examine walking performance, user experience, physical exertion, motion sickness and presence among the participants using either the proposed gait model or existing gait models. The student will assist with study design, participant recruitment, and data collection and analysis.

By leading this project, the student will gain the skills needed to conduct research, develop VR applications, and design novel locomotion methods. They will also help disseminate findings through presentations or manuscript preparation.

Skills required:
This project requires students with experience using Unity and C# to develop a 3D application. Students need a background in computer science or its equivalent and feel comfortable with object-oriented programming. They should also have experience working with different SDKs, as this project requires them to use multiple SDKs at the same time.

A background in biomechanics/rehabilitation/kinesiology, or previous VR development, is not required, but a plus.

Research area: I am expert in fluid mechanics for energy systems, bioengieering and coastal applications, aerodynamics, etc. My research concerns mainly the development of advanced numerical modelings, from optimization algorithm coupled to 1D models to 3D direct numerical simulations for any problems involving heat and mass transfer and fluid flow. Part of my research focuses also on characterizing the thermophysical properties of complex fluids (phase change materials, slurries, nanofluids, drilling fluids, bioinspired fluids) for a wide range of applications.

Student roles:
Do the literature review on hyperthermia methods, magnetocaloric effect and materials;
Use an existing numerical model to predict the performance of nanoparticles to induce hyperthermia and perform a parametric analysis (concentration, size and type of nanoparticles, frequency);
Extend the method to model more accurately the magnetocaloric effect;
Couple it with an optimization algorithm and optimize the process;
Write a technical report.

Skills required:
Good knowledge in basic fluid dynamics and heat transfer

Research area: My research operates at the intersection of Empirical Software Engineering and Artificial Intelligence for IT Operations (AIOps). Specifically, our lab focuses on software logging infrastructure, performance engineering, and the integration of Artificial Intelligence into software maintenance. A major current focus is the application and evaluation of recent open-weight LLMs and Agentic AI for automated software engineering tasks. We aim to make complex software systems and the AI agents that help maintain them more reliable, observable, and trustworthy through rigorous empirical evaluation and innovative data analysis techniques.

Student roles:
The student will play a hands-on, integral role in our empirical software engineering research. Day-to-day responsibilities will be diverse and highly collaborative.

First, the student will engage with current research literature to understand the state-of-the-art in LLM observability and AIOps. The core of the internship will involve programming and scripting (primarily in Python) to set up automated pipelines that interact with recent open-weight LLMs. The student will run controlled experiments, tasking these AI agents with specific software engineering challenges, and capturing their execution traces.

A significant portion of the role will be analytical. The student will be responsible for parsing and analyzing the generated data and execution logs to identify patterns, anomalies, and performance bottlenecks. This will involve writing scripts to clean and format data, as well as applying empirical analysis techniques.

Throughout the project, the student will have regular, dedicated meetings with the supervisor and other researchers in the lab to discuss progress, troubleshoot technical roadblocks, and refine experimental designs. Finally, the student will synthesize their findings by creating visual models of the LLM execution paths and contributing to the drafting of a final research report. This role provides a comprehensive introduction to the full lifecycle of academic computer science research.

Skills required:
Ideal candidates should have a strong foundation in Computer Science or Software Engineering. Proficiency in programming, particularly in Python, is required, as the student will be writing scripts to interact with AI models and analyze data. A background in machine learning and deep learning is highly desirable, along with familiarity with Large Language Models (e.g., interacting with APIs or open-source models). Experience with data parsing, software logging, or empirical research methods is considered a strong asset. Most importantly, the student should possess strong problem-solving skills and a curiosity for investigating how complex systems operate.

Research area: My research develops algorithms for decision-making under uncertainty, with applications to networked systems, such as smart grids, cloud computing and LLM inference systems. The central theme of my work is to understand how algorithm performance scales with the structural properties of the underlying systems (e.g., objectives, constraints, online dynamics, and multi-agent interactions), and the nature of environmental uncertainties (e.g., adversarial, stochastic, or predictive uncertainty). My goal is to design algorithms with provable guarantees for efficiency, robustness and fairness, enabling reliable resource allocation and scheduling in real-world networked systems.

Student roles:
The student will play an active role in both the theoretical and empirical components of the project. The main responsibility of the student is to study online scheduling problems in LLM inference systems, where requests arrive sequentially over time and must be scheduled under limited memory capacity.

On the theoretical side, the student will formulate online scheduling models that capture key structural properties of LLM inference, including autoregressive generation, dynamic KV-cache memory growth, and uncertain output lengths. The student will study related literature in online algorithms, scheduling, and LLM serving, and identify the limitations of existing scheduling policies in this new setting. Based on these insights, the student will contribute to the design and analysis of customized online scheduling algorithms with provable performance guarantees, such as competitive-ratio bounds.

On the empirical side, the student will implement the proposed scheduling policies and compare them with commonly used baselines, such as first-in-first-out and shortest-job-first. The student will build simulation environments or use existing LLM serving traces to evaluate algorithm performance. This may involve processing request traces, modeling memory consumption, running experiments, and analyzing the trade-offs among latency, throughput, and memory efficiency.

The student will also participate in regular research meetings, present progress, discuss technical challenges, and help prepare research outputs, including project reports, presentation materials, and potential conference or journal submissions. Through this project, the student is expected to develop both algorithmic research skills and practical experience in system evaluation. The role is suitable for a student who is interested in online algorithms, scheduling, and the emerging infrastructure challenges behind modern LLM inference systems.

Skills required:
The student is expected to have a solid mathematical background, including linear algebra, calculus, probability, and basic optimization. They should have a strong foundation in algorithm design and analysis. Prior experience in online algorithms, approximation algorithms, scheduling, or queueing systems is preferred but not required.

The student should also have good programming skills and be proficient in at least one programming language, such as Python. Experience with simulation or machine learning frameworks would be helpful. Familiarity with large language models or inference systems is a plus, but not required.

Research area: My specialized research area is wireless communications and intelligent network optimization. I focus on resource management (RRM), scheduling, and resource allocation in dynamic wireless systems under QoS, latency, and energy constraints. My work relies on mathematical modeling, optimization theory, and algorithm design to efficiently manage limited resources such as power, bandwidth, and time in large-scale networks. I also develop scalable solutions for time-varying and uncertain environments. More recently, I integrate machine learning and reinforcement learning methods to design adaptive and data-driven algorithms for wireless control and decision-making. Overall, my research combines wireless systems, optimization, and AI to enable efficient next-generation

Student roles:
The student will play a key role in a research project focused on online optimization of Age of Information (AoI) in dynamic wireless communication systems. The main objective is to design, analyze, and implement efficient algorithms that ensure timely and fresh information delivery while minimizing energy consumption under strict system constraints.

The student will begin by conducting a literature review on AoI, wireless scheduling, online optimization, and resource allocation. This will help identify key challenges and state-of-the-art approaches in real-time communication systems.

The student will then contribute to the mathematical modeling of the system, including packet arrival processes, transmission decisions, energy consumption models, and AoI evolution. Based on these models, the student will help formulate online optimization problems and design causal policies that operate without future knowledge of arrivals or channel states.

A major part of the role involves algorithm development and implementation. The student will design and test scheduling and transmission strategies, including optimization-based and potentially learning-based methods. Implementation will be done in Python and/or Julia, with extensive simulations to evaluate performance under different network scenarios.

The student will also analyze theoretical guarantees when applicable, such as competitive ratios or approximation bounds, and compare proposed methods against baselines to quantify trade-offs between AoI and energy efficiency.

In addition, the student will participate in regular research meetings, present progress, and contribute to technical reports and research papers. The role is research-intensive and aims to develop strong expertise in wireless networks, optimization, and algorithm design for next-generation communication systems.

Skills required:
The candidate should have a background in Electrical Engineering, Computer Engineering, Computer Science, or a related field, with strong foundations in wireless communications, networking, probability, and optimization. Knowledge of scheduling, resource allocation, and Age of Information (AoI) is highly desirable. Strong mathematical and analytical skills are required for system modeling and algorithm design. Proficiency in programming, particularly in Python and/or Julia, is essential, along with experience in scientific computing and simulation. Knowledge of machine learning and reinforcement learning is an asset. The ideal candidate should be motivated, independent, and curious, with a strong interest in theoretical and applied research.

Research area: My specialized research area focuses on advancing open Radio Access Networks (RANs) for the next generation of cellular networks beyond 5G. In this field, I address the complex challenge of optimizing performance and ensuring quality of service by efficiently controlling radio resources within the framework of open RAN architectures. By leveraging software-defined and programmable RAN components through standard open interfaces, my research aims to revolutionize network flexibility, scalability, and interoperability. This cutting-edge work is essential for shaping the future of cellular networks, enabling seamless connectivity and enhanced user experiences in the evolving digital landscape.

Student roles:
As a student involved in this project, your role will be multifaceted and critical to its success. You will begin by conducting a comprehensive review of existing literature and research findings related to securing AI/ML in RAN environments. This will provide you with a solid foundation for understanding the challenges and potential solutions in this domain.

Next, you will collaborate with the research team to design and implement a testbed environment specifically tailored for evaluating AI/ML security within ORAN. This will involve configuring network infrastructure, deploying AI/ML models, and integrating security mechanisms to protect against potential threats.

Once the testbed is operational, you will lead the vulnerability assessment phase, systematically identifying and analyzing potential attack vectors and vulnerabilities within the AI/ML components of the RAN. Using your analytical skills and technical expertise, you will devise and implement countermeasures to mitigate these risks effectively.

Throughout the project, you will conduct rigorous testing and evaluation to validate the effectiveness of the implemented security measures. This will involve simulating various attack scenarios, assessing their impact on network performance and integrity, and refining defense strategies as needed.

Additionally, you will document your findings, methodologies, and recommendations in technical reports and presentations, contributing valuable insights to the broader research community. Your contributions will be instrumental in advancing our understanding of AI/ML security in RAN environments and informing the development of future mobile communication systems.

Skills required:
We are seeking highly motivated students with a background in mobile communications, network security, or AI/ML. Candidates should have strong analytical skills, programming proficiency (e.g., Python, C/C++), and familiarity with network protocols and machine learning algorithms. Experience in conducting vulnerability assessments and using testbeds is advantageous.

Research area: My specialized research area is the design and application of artificial intelligence, machine learning, data analytics, and decision-support systems for complex real-world problems. My work includes the development of computational methods for extracting useful information from heterogeneous data sources, building predictive or analytical models, and transforming raw data into interpretable outputs that can support evidence-informed decision-making. More broadly, my research lies at the intersection of artificial intelligence, data science, optimization, and applied decision-support systems, with applications in areas such as health, resilience, emergency preparedness, and public-sector analytics.

Student roles:
The student will contribute to the design and implementation of a proof-of-concept dashboard for health infrastructure resilience and emergency preparedness. Their role will include identifying relevant open data sources, reviewing documentation for selected datasets, collecting and cleaning country-level indicators, and organizing the data into a structured format suitable for analysis and visualization.

The student will implement a data-processing pipeline, develop exploratory analyses, and build an interactive dashboard prototype. They will help define and test simple standardized indicators, such as health infrastructure capacity, workforce availability, or potential surge vulnerability, while carefully documenting assumptions and limitations. The student will also prepare visual summaries, write technical documentation, and contribute to a final report describing the dashboard, data sources, methodology, findings, and future extensions.

Depending on the student’s background and progress, the project may also include simple statistical or machine-learning analyses, such as clustering countries with similar health-system profiles, detecting missing-data patterns, or identifying trends in preparedness-related indicators.

Skills required:
The student should have a strong interest in data science, artificial intelligence, public-sector analytics, and applied decision-support systems. Good Python programming skills are required, including experience with data cleaning, analysis, and visualization. Familiarity with pandas, NumPy, matplotlib, Plotly, Dash, Streamlit, scikit-learn, APIs, or geospatial visualization would be useful. Experience with public health data or web dashboards would be an asset, but is not required. The ideal student should be comfortable working with messy real-world datasets, reading technical documentation, and clearly communicating data sources, assumptions, limitations, and analytical results.

Research area: My academic background is in mathematics and physics. More specifically, my research area is in nanoscience/nanotechnology and I'm currently working on the development of water quality sensors using optical spectroscopy.

Student roles:
The student would assist in the development of water quality sensors joining a team of interdisciplinary researchers and may be assigned different tasks depending on their background/skills (e.g. data analysis, literature research, measurements, lab work, instrumentation, experimental work, optical spectroscopy, sensor technologies, water quality measurement techniques, device design, computer programing, modeling, and machine learning).

Skills required:
The project is interdisciplinary and interns with various backgrounds could contribute to the research with their own expertise. E.g., chemistry, physics, engineering, math, and computer science are all related. Assets include experience with the following: data analysis, literature research, measurements, lab work, instrumentation, experimental work, optical spectroscopy, sensor technologies, water quality measurement techniques, device design, computer programing, modeling, and machine learning.

Research area: My research interests include developing mathematical models and employing advanced optimization techniques to solve challenging decision problems in a wide range of areas including sustainable smart cities (energy, water, transportation, and telecommunication networks). My current research focuses on optimizing intelligent energy and transportation networks to derive data-driven insights and decisions through exploring uncertain and complex data, anticipating various scenarios, and applying optimization tools to recommend actions and solutions leading to better performance.

Student roles:
The student will gain extensive experience in data mining and AI techniques as well as optimization and mathematical modeling. He/she will work with real mobility and traffic data from different sources and integrate state-of-the-art AI techniques to develop a smart and sustainable transportation system.
In particular, the student will have the following tasks: (i) visually analyze, assess quality, and manipulate heterogeneous data; (ii) construct and implement different prediction models to detect mobility patterns based on historical GPS positions of the users; (iii) develop mathematical optimization models that use the predicted mobility patterns as an input with the aim of reducing road congestion and improving users' experience.

Skills required:
Required Skills:
- Proficient programming skills in a high-level language such as Python or C
- Basic knowledge of linear algebra, probability, and statistics

Additional Preferred Skills:
- Knowledge of machine learning algorithms and tools
- Knowledge of optimization models
- knowledge in reinforcement learning

Research area: My research is on functional programming and dependent types. Specifically, I'm interested in ways that we can use types to ensure that programs are free of bugs, and to use types to guide the development of programs in a structured, rigorous way. Such type systems are sometimes difficult to use, so my research aims to develop advancements which improve the usability of such tools.

Student roles:
Work on the project will be in four phases:

(1) Background: the student will learn about the Idris language and its compiler internals, as well as relevant existing research (e.g. stratified type theory, dynamic size-change tracking, etc.)

(2) Design: the supervisor and student will collaborate to determine how exactly dynamic tracking should be added to the compiler, and what code must be changed to enable this.

(3) Implementation: the student will modify the Idris compiler source code to implement a prototype version of dynamic termination checking.

(4) Evaluation: the compiler will be tested on programs which are known to be terminating or non-terminating, to verify its accuracy and evaluate the usefulness of dynamic information it gives.

Since the modifications are experimental, challenges or roadblocks may be encountered during the implementation, that will require re-evaluating the design. Such discoveries are also important research contributions, and the student will be encouraged to experiment and try different paths to see what works.

Skills required:
The student should be familiar with typed functional programming, algebraic datatypes, and higher-order functions, as well as the basic concepts of programming language implementation (syntax trees, tree-walking interpreters, recursion, etc.).

While not required, any familiarity with proof assistants or dependently typed languages is an asset (e.g., any experience with Agda, Idris, Lean or Rocq). Likewise, it is not required, but any knowledge of abstract math topics would be helpful (e.g., abstract algebra, category theory, lattices/order theory, proofs/induction, graph theory, or discrete math)

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: This research area advances intelligent network security by integrating interpretable multi-model machine learning, differential privacy, federated learning, and privacy-preserving authentication for distributed IoT and cloud ecosystems. The program spans three interconnected pillars: interpretable threat detection using structured feature selection combined with diverse classifier families for DDoS attack identification; privacy-preserving hierarchical fog federated learning for scalable IoT intrusion detection under non-IID data distributions; and formal privacy mechanism evaluation and non-cryptographic authentication satisfying ISO/IEC 24745. Together, these pillars establish trustworthy, formally private, and deployable intelligent systems for next-generation distributed network security.

Student roles:
The student will take an active role in implementing and evaluating components of the PIFDA framework across the three research thrusts. In the multi-classifier DDoS detection thrust, the student's primary responsibilities will include implementing and integrating the ten classifier families and seven feature selection techniques into a modular evaluation pipeline, running experiments on the KDD-99 dataset, and analyzing the accuracy-efficiency trade-offs across the full classifier-selector combinatorial space. The student will produce systematic comparison tables, generate statistical significance tests, and document findings to support manuscript preparation targeting network security and applied machine learning venues. In the PP-HFFL federated learning thrust, the student will assist in implementing the hierarchical fog architecture, coding the FedAvgDP algorithm with Opacus-controlled differential privacy, and running scalability experiments across fog node counts ranging from 10 to 400 clients on the RT-IoT 2022 and CIC-IoT 2023 benchmarks. The student will implement the personalized federated learning objective, tune the regularization parameter lambda under non-IID data conditions, and validate improvements via paired t-tests and Wilcoxon signed-rank tests. In the privacy mechanism taxonomy thrust, the student will assist in designing and executing comparative experiments across Trusted Execution Environments, Secure Multi-Party Computation, and Differential Privacy paradigms, populating the applicability matrix, and contributing to the formal privacy evaluation protocol aligned with ISO/IEC 24745. Beyond technical contributions, the student will engage in knowledge mobilization by assisting in drafting scientific manuscripts, creating conference presentations, and maintaining thorough documentation of all experimental configurations and results. These activities will strengthen academic writing and professional communication skills. Through this project the student will develop expertise in federated learning, differential privacy, network threat detection, and statistical validation, alongside critical soft skills in research organization, collaborative problem-solving, and scientific communication, preparing them for careers in AI-driven security or privacy-aware distributed systems.

Skills required:
The student should have foundational knowledge in machine learning and network security concepts, with experience in Python programming. Familiarity with classification algorithms, data preprocessing, and evaluation metrics is essential. Basic understanding of networking protocols, traffic analysis, or cybersecurity concepts is an asset. Prior exposure to federated learning, differential privacy, or deep learning frameworks such as PyTorch or TensorFlow is beneficial but not required. The student should be detail-oriented, capable of systematic experimental evaluation across multiple classifiers and datasets, and willing to engage with privacy and security literature. Computer science, software engineering, or information security backgrounds are encouraged.

Research area: This project is under the context of an initiative to improve the efficacy of post-quantum algorithms executing on architectures with constrained resources.

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:
C programming, RTL, HLS, FPGA prototyping, testbench

Research area: My research lies at the intersection of artificial intelligence, high-performance computing, software engineering, compiler technologies, and scientific computing. It investigates how compiler-guided retrieval and large language models can transform sequential scientific applications into correct and scalable parallel implementations for modern multicore systems. The work addresses limitations of current AI code-generation systems in producing performance-critical software where correctness and scalability are essential. By integrating compiler-guided retrieval, domain-specific knowledge repositories, and automated performance feedback, the research aims to improve the reliability of AI-assisted software development and advance intelligent tools that make parallel computing more accessible to scientists and engineers.

Student roles:
The student will play an active role in the design, implementation, and evaluation of the PSGA framework. With three internship positions available, students may focus on complementary project components, including repository construction, retrieval-augmented code generation and compiler-feedback integration, or benchmarking and performance analysis. Each student will contribute to a shared PSGA prototype while maintaining ownership of a distinct research component. The internship will provide hands-on experience at the intersection of artificial intelligence, compiler technologies, software engineering, and high-performance computing.

The student will assist in building and organizing a retrieval repository containing verified OpenMP implementations, compiler optimization guidance, scientific computing examples, and parallel programming patterns. They will help develop retrieval pipelines that identify relevant knowledge for code generation tasks.

A major component of the internship will involve implementing retrieval-augmented code generation workflows and integrating compiler feedback into the software development process. The student will work with modern large language models and explore how compilation diagnostics, correctness testing, and performance measurements can guide iterative code refinement. Through this process, they will gain practical experience with AI-assisted programming and automated software optimization.

The student will conduct experiments using sparse Jacobian computation benchmarks from the DSJM framework. Responsibilities will include preparing benchmark workloads, executing experiments, collecting performance measurements, and analyzing results related to correctness, speedup, scalability, and parallel efficiency. They will also contribute to the development of reproducible benchmarking pipelines and experimental documentation.
In addition to technical development, the student will participate in regular research meetings, literature reviews, and collaborative discussions. Project outputs will be disseminated through reproducible software artifacts, benchmark datasets, technical reports, open-source releases, and, where appropriate, conference publications and presentations.

Throughout the internship, the student will receive mentorship in artificial intelligence, retrieval-augmented generation, compiler technologies, parallel programming, and scientific software engineering. This experience will help develop both research and technical skills.

Skills required:
The ideal student will have strong programming and analytical skills with interests in artificial intelligence, software engineering, and scientific computing. Experience with Python, C++, or related languages, together with a solid foundation in data structures, algorithms, and software development practices, is desirable. Familiarity with Linux environments, Git, debugging, software testing, machine learning, large language models, information retrieval, or natural language processing is beneficial. Exposure to parallel computing, OpenMP, compiler technologies, or high-performance computing environments is considered an asset. Students from computer science, software engineering, artificial intelligence, data science, computational mathematics, or related disciplines are encouraged to apply.

Research area: Image Processing Computer Vision Machine Learning Artificial Intelligence Medical Image Analysis Learning Analytics

Student roles:
Designing and developing system to apply in distance education
Reviewing research articles, and implementing and analyzing systems for performance evaluation
Writing report and research articles
Doing project presentations in the progress meeting

Skills required:
Programming experiences and skills in Matlab or Python
Experience and ability to design and build tools or systems
Experience in designing user interfaces and data visualization
Student must have knowledge in machine learning and pattern classification
Experience in information collection, re-organization, summarization, and preparing reports
Good communication skill

Research area: My specialized research area focuses on ethical, inclusive, and pedagogically grounded artificial intelligence in literacy education, with a particular emphasis on how customized AI writing tools, can support elementary and high school students' writing development, student voice, critical thinking, engagement, and writer identity. This work is grounded in the AIME architecture (Ewart & Mueller, 2026), which showcases adaptive and differentiated learning, culturally responsive AI integration, multiple and critical AI literacies, and experiential, ethical, and evolving AI practices. Through the Pen-to-Prompt project, my research examines how AI can function as a scaffolded writing companion rather than a replacement for student authorship.

Student roles:
The research assistant will support the Pen-to-Prompt project by assisting with research coordination, data organization, literature review development, and analysis related to AI-supported writing instruction in elementary and secondary classrooms. The student will help manage and organize research materials, including student writing samples, teacher reflections, AI session logs, classroom observation notes, interview/focus group data and project documentation. The research assistant may also support the coding and analysis of qualitative data, assist with organizing pre-during-and post writing samples, contribute to research summaries, help prepare conference or publication materials, and support knowledge mobilization for school boards and teacher education audiences.

Skills required:
The research assistant should have a background or strong interest in K-12 education, literacy, education technology, and artificial intelligence. The student should be familiar with qualitative and/or quantitative and/or mixed-methods research methods and demonstrate strong organizational, writing, and communication skills. Experience with literature reviews, data organization, coding qualitative data, analyzing student writing samples, preparing research summaries, and supporting REB-aligned research processes. The student should also have an interest in ethical and inclusive AI integration, multiliteracies, and K-12 writing instruction. Because the project involves elementary classrooms, student writing, and reflections, the student must be attentive to confidentiality.

Research area: I specialize in computer graphics, virtual humans, crowd simulation, and immersive virtual reality. My research focuses on building realistic and interactive digital environments where virtual characters can perceive, move, communicate, and respond to users in socially believable ways. This includes emotion-aware avatars, pedestrian and crowd behaviour, embodied interaction in VR, real-time animation, and simulation-based evaluation of human movement and social spacing. The broader goal of my work is to create intelligent virtual environments for games, film-making, training, digital twins, education, and human-centred simulation.

Student roles:
The student will help create and evaluate Unreal Engine scenes for studying how users perceive emotion in virtual crowds. At the beginning of the internship, the student will review related work on emotional crowd perception, facial and body expression, crowd size, and user perception in virtual environments. Based on this review, the student will help finalize the experimental conditions, including crowd size, emotion type, animation modality, and percentage of emotional characters.

The student will build Unreal Engine scenes using MetaHumans and prepare short video clips showing emotionally expressive crowds. The first set of clips will compare three presentation conditions: body motion only, facial expression only, and full motion with both face and body cues. These clips will be created for different crowd sizes, such as 6, 12, 20, 32, and possibly 52 characters, depending on performance. The student will help ensure that camera placement, lighting, timing, and scene layout remain consistent across conditions.

The student will also help design a second experiment that varies the percentage of emotional characters in the crowd. This experiment will use the most effective emotion presentation method and the most suitable crowd size identified in the first experiment. The goal is to determine how many emotional characters are needed before users can reliably perceive the crowd’s overall emotional state.

The student will assist with preparing the study interface, collecting participant responses, and analyzing results such as emotion recognition accuracy, response time, confidence, and perceived naturalness. The student may also help generate plots, summarize findings, and prepare demo videos.

By the end of the 12-week internship, the student is expected to deliver Unreal Engine scenes, generated video stimuli, documented project files, preliminary user-study results, and a short technical report.

Skills required:
The student should have a background in computer science, game development, computer graphics, animation, human-computer interaction, psychology, or a related area. Experience with Unreal Engine is required or strongly preferred. Familiarity with MetaHumans, character animation, facial expressions, motion capture, Blueprints, C++, or Python would be an asset. The student should be interested in virtual humans, crowd simulation, perception, games, or VR. Basic knowledge of experimental design, data collection, or statistical analysis is helpful but not required.

Research area: My research focuses on computer networking and network measurement, with a particular interest in modern cloud networking architectures. My expertise lies in evaluating the performance, reliability, and security of large-scale networks, including hybrid and multi-cloud environments. I combine theoretical knowledge with practical cloud skills (AWS and GCP) to study how real-world cloud networks behave under different conditions.

Student roles:
The students will play a central hands-on role in the project and will be responsible for:

Setting up and configuring AWS Cloud WAN and Google Cloud Interconnect test environments
Designing and implementing automated measurement experiments
Collecting, processing, and analyzing large volumes of network performance data
Comparing results across different scenarios and visualizing findings
Contributing to the writing of a research paper (literature review, methodology, results, and discussion sections)
Participating in regular project meetings and presenting progress

Skills required:
We are looking for a motivated student with:
- understanding of computer networking fundamentals (TCP/IP, routing, congestion control, etc.)
- experience with Linux command line
- Good programming skills in Python (for scripting experiments and data analysis)
- Interest in cloud computing and network performance measurement
- Strong analytical and writing skills

Previous hands-on experience with AWS or Google Cloud is an asset but not mandatory. The student will receive training and guidance on cloud platforms.

Research area: Our research group is generally interested in designing and evaluating interactive systems such as Games, Mobile applications, Social Media applications, and Web Applications, and Personalized and Adaptive Systems to solve real-life problems. These are active Human-Computer Interaction (HCI) research areas. We are mostly interested in using these systems to solve problems that are of global importance especially in the area of health and wellness (e.g, promoting a healthy lifestyle, physical activity, healthy eating, smoking cessation) and safety and security (e.g., avoiding risky driving behaviour). Problem domains such as energy conservation and climate change are also of interest.

Student roles:
The student will join a team of faculty, postdoctoral researchers and graduate students working on various projects at the Human-Computer Interaction Lab. The student would work with graduate students on projects that involve developing and evaluating persuasive systems. There is flexibility for the students to focus on a particular application; games, mobile application, social media and web application design or some combinations. The student would have access to collections of computer science papers and other relevant literature and resources (software and hardware) needed for the project. We hope to publish at least a fully refereed paper at the end of the project and with additional work to be carried out remotely after the end of the Globalink visit we could possibly generate more publications.

Skills required:
The student needs to have a strong programming and design skills, be interested in working with people. Previous experience with games, mobile application, social media and web application design and development will be of advantage. Familiarity with or interest in pursuing graduate studies in one of the following areas would be highly desirable: human-computer interaction, web intelligence, computer graphics, visualization, data analytics, machine learning, text mining, information retrieval.

Research area: This research focuses on model-based and learning-enhanced control of cooperative tendon-driven continuum robots. These robots are highly flexible and suitable for manipulation in constrained or unstructured environments, but their control is challenging due to nonlinear dynamics, tendon friction, hysteresis, external loading, and closed-chain constraints. By combining Geometric Variable Strain modeling with physics-informed neural networks, this work aims to improve real-time control accuracy, robustness, and adaptability for cooperative continuum robotic systems.

Student roles:
The student will be responsible for developing the simulation and learning pipeline for the proposed project. This includes studying the existing GVS-based continuum robot model, generating training data, implementing physics-informed neural networks, and integrating the trained model with a control framework. The student will also perform simulation studies to evaluate tracking accuracy, robustness, and generalization under different uncertainties and loading conditions.

The student is expected to document the methodology, organize simulation results, participate in regular research meetings, and contribute to the preparation of reports, presentations, and potential publications. Collaboration with the research team will be required throughout the project.

Skills required:
• Background in robotics, control systems, mechanical engineering, or machine learning.
• Proficiency in Python and experience with neural network frameworks such as PyTorch or TensorFlow.
• Familiarity with MATLAB/Simulink for simulation and control implementation.
• Basic understanding of nonlinear dynamics, adaptive control, robust control, or physics-informed neural networks.
• Knowledge of continuum robots, soft robots, or tendon-driven robotic systems is an asset.
• Ability to work independently, document results clearly, and collaborate with the research team.

Research area: My research focuses on understanding and advancing freezing processes, particularly for renewable energy applications in cold climates. I’ve developed multi-scale solidification models that accurately capture equilibrium and non-equilibrium behaviors across macro to micro scales. These frameworks help predict emerging freezing phenomena and guide innovation in cold-region technologies. I’ve also led advancements in Spray Freezing and Artificial Ground Freezing through experiments, modeling, and sensitivity analysis. My work supports infrastructure stability, permafrost preservation, and decarbonization efforts in northern communities, contributing to both climate resilience and resource protection.

Student roles:
Weeks 1–3: Review literature on PINNs and formulate a heat transfer problem.
Weeks 4–6: Implement a PINN to solve the forward heat conduction problem.
Weeks 7–9: Extend the model to solve an inverse problem using sparse data.
Weeks 10–12: Evaluate performance, optimize the model, and prepare a final report.

Skills required:
- Very familiar with python
- Familiar with partial differential equations
- Strong in heat transfer or fluid mechanics (or willing to learn)

Research area: This project lies at the intersection of robotics, machine learning, and welding metallurgy. Modern welding and metal-deposition processes remain largely open-loop, where quality is assessed only after production. Our research investigates how robots can use real-time sensor data, including vision, depth, acoustics, and process signals, to monitor manufacturing conditions and adapt their behavior autonomously. By integrating machine learning, materials science, and robotic control, the project aims to establish foundations for intelligent robotic systems capable of maintaining manufacturing quality through closed-loop decision-making in welding applications.

Student roles:
The student will participate in all stages of the research project, from data collection and processing to machine-learning model development and experimental evaluation. Working closely with the research team, the student will assist in operating a robotic welding platform equipped with multiple sensing modalities, including RGB cameras, depth sensors, and process-monitoring instrumentation.

The student’s primary responsibilities will include organizing and analyzing experimental datasets, developing software tools for data processing and visualization, and implementing machine-learning models for manufacturing applications. The student will investigate state-of-the-art robot-learning techniques, including imitation learning, transformer-based architectures, and multi-modal learning approaches, to model the relationship between robot actions, sensor observations, and weld-quality outcomes.

The student will conduct experiments to evaluate model performance, compare different learning approaches, and identify key factors that influence manufacturing quality and process consistency. They will contribute to the development of reproducible research workflows, maintain documentation of experiments and results, and present findings during regular project meetings.

Throughout the internship, the student will gain hands-on experience in robotic welding, artificial intelligence, computer vision, and advanced manufacturing. They will work in an interdisciplinary research environment and interact with graduate students and faculty members specializing in robotics, machine learning, and materials processing. The student will also contribute to the preparation of technical reports, research publications, and presentations that disseminate project outcomes to both academic and industrial audiences.

This role provides an opportunity to apply modern AI techniques to a real-world manufacturing challenge while developing practical research skills in experimental robotics, data-driven modeling, and intelligent automation.

Skills required:
The student should possess strong analytical and problem-solving skills, be comfortable working with experimental data, and demonstrate the ability to work independently and collaboratively within an interdisciplinary research environment. The ideal student should be pursuing studies in materials engineering, computer science, metallurgy, mechanical engineering, manufacturing engineering, mechatronics, robotics, or a related discipline. Experience with Python programming is required. Familiarity with machine learning, robotics, computer vision, data analysis, or control systems is desirable. Knowledge of ROS, deep learning frameworks (PyTorch or TensorFlow), and sensor data processing would be beneficial but not mandatory.

Research area: I'm specialized in machine learning.

Student roles:
- Literature Review: Conduct a comprehensive review of existing work on relevant methodologies.
- Model Implementation: Develop and implement models using deep learning frameworks (e.g., JAX). This includes adapting existing architectures and designing new components tailored to specific tasks.
- Designing and carrying out experiments on benchmark datasets, evaluating model performance across various scenarios.
- Analyzing experimental results to assess model effectiveness and provide insights into the behavior and limitations of the approaches.
- Writing well-documented code and progress reports, preparing manuscripts or presentations if applicable.

Skills required:
The student should have
1. Strong background in machine learning, particularly in reinforcement learning and diffusion models.
2. Proficiency in Python and experience with ML frameworks such as JAX or PyTorch.
3. Effective communication skills in both writing and speaking in English.

Research area: This research area focuses on culturally aware artificial intelligence, combining natural language processing, knowledge graphs, and machine learning. It investigates how cultural knowledge, beliefs, and social contexts can be represented computationally and structured into interpretable models. The area includes graph-based and hierarchical representations, as well as neural approaches for reasoning over structured data. A key objective is to improve how AI systems, particularly large language models, understand, compare, and align with diverse human perspectives in complex, real-world environments.

Student roles:
Students will contribute to multiple components of the PluralPath framework, taking on complementary roles across data extraction, literature analysis, modeling, and evaluation.
One role focuses on data-driven extraction, where students will apply natural language processing and data mining techniques to identify nodes and relations from large-scale text corpora. They will analyze how these elements organize into hierarchical structures by estimating centrality, importance, and contextual variation. Students will then group nodes and relations to derive emergent graph schemas and multi-level representations of cultural pathways.
A second role emphasizes literature-driven modeling, where students will collect and analyze academic work from both AI and social science domains. They will map connections between researchers, tasks, and cultural interpretations, and use generative AI tools to extract higher-level schema components and reasoning pathways. A key objective is to compare how cultural understanding is structured across disciplines and identify gaps or inconsistencies.
A third role involves agentic modeling and analysis, where students will design methods to compare and reconcile differences between data-driven and literature-driven graph schemas. This includes identifying conflicts, noise, and missing structures, and studying how perspectives differ between empirical data and expert interpretations.
A fourth role focuses on neural modeling, where students will develop and evaluate hierarchical neural networks, including hyperbolic tree-based models with gating mechanisms. These models will be used to measure similarity and divergence across cultural pathways and support task-specific reasoning.
Finally, students will participate in task analysis and evaluation, identifying relevant cultural reasoning tasks, benchmarking datasets, and assessing how structured pathways can enhance LLM performance. Throughout the project, students will contribute to experimentation, analysis, and reporting.

Skills required:
Strong Python skills required. Background in machine learning, NLP, or data science preferred. Knowledge of graphs, neural networks, or mathematical modeling is an asset. Interest in interdisciplinary AI + social science research is important.

Research area: My research focuses on governed autonomy: how AI agents can safely perform operational tasks in regulated software and infrastructure environments. I work on agentic AI architectures, the Model Context Protocol (MCP), runtime policy enforcement, and audit mechanisms that make autonomous actions verifiable and compliant. This includes policy-as-code controls, risk-based approval workflows, and AI lifecycle governance aligned with NIST AI RMF and ISO/IEC 42001. Combining enterprise AI platform leadership with academic research, I supervise projects that produce empirical evidence on a question regulated industries are asking now: how much autonomy can be delegated to AI, under which controls?

Student roles:
The intern will design and operate the full evaluation environment.
Weeks 1–4: build the synthetic pipeline environment and author the policy set encoding change-management controls, with supervisor guidance on the control framework.
Weeks 5–8: integrate the LLM agent, execute change-request campaigns across risk grades, and collect baseline metrics.
Weeks 9–12: analyze interception precision, false-block rates, and completion trade-offs; package the open-source evaluation framework; co-author the paper draft.
The student will join weekly supervision meetings, maintain reproducible version-controlled experiments, and deliver a final presentation of findings.

Skills required:
Strong Python skills and solid software engineering fundamentals: Git, CI/CD concepts, containers, and scripting. Familiarity with infrastructure-as-code (e.g., Terraform, Ansible) or configuration management is a strong asset; it can be learned quickly if fundamentals are solid. Exposure to LLM APIs or agent frameworks is helpful but not required. No prior knowledge of Open Policy Agent or Rego is expected; this will be taught. Interest in DevOps, software supply-chain integrity, or AI safety is essential, as is the discipline to run controlled experiments and document results rigorously.

Research area: Dr. Xiaoxiao Li is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) and CIFAR AI Chair at Vector Institute. Dr. Li is recognized as a Canada Research Chair (Tier II) in Responsible AI. Dr. Li's research aims to enhance the trustworthiness and efficiency of AI models, bridging the gap between cutting-edge AI research and practical real-world applications. Dr. Li's current interests include mechanistic analysis of large language and vision-language models (LLMs/VLMs), developing hypothesis-driven evaluations, and advancing methodologies toward artificial general intelligence (AGI).

Student roles:
The intern student will be assigned a detailed topic. The student is encouraged to explore the technical details during implementation. The student is required to attend regular project meetings during the internship. The intern student should report progress in the midterm and at the end of the internship.

Skills required:
The ideal student should be proficient in Python and have experience with deep learning using frameworks such as PyTorch. A solid understanding of deep learning fundamentals and large language models is highly desirable. Familiarity with reinforcement learning, knowledge distillation, or efficient inference techniques would be a significant asset, although not strictly required. We welcome students from related disciplines such as computer science, mathematics, statistics, or engineering who have a strong interest in efficient machine learning research.

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:
The student will take an active research and engineering role in developing post quantum zero knowledge proof (PQ ZKP) protocols for decentralized Web3 environments. Their work will begin with understanding the foundations of post quantum cryptography, zero knowledge proofs, and lattice based security. They will analyze existing PQ ZKP constructions and identify limitations related to proof size, verification cost, and communication overhead in peer to peer networks.

The student will design and prototype new or optimized PQ ZKP schemes using lattice based commitments and quantum resistant assumptions. This includes implementing provers and verifiers, integrating them with peer to peer networking frameworks such as libp2p, and enabling privacy preserving authentication and reputation mechanisms for Web3 applications. They will evaluate protocol performance under realistic network conditions, measure computational and communication efficiency, and compare results with classical ZKP systems.

The student will also contribute to documentation, experimental analysis, and preparation of research outputs. They will collaborate with the supervisor to refine protocol designs, interpret results, and support the development of an open source prototype demonstrating PQ ZKP based identity and trust mechanisms for decentralized systems.

Skills required:
The student should have a strong foundation in cryptography, including familiarity with post quantum security and basic understanding of zero knowledge proofs. Knowledge of lattice based cryptography, especially LWE and Module LWE, is an asset. Solid programming skills in Python, Go, or TypeScript are required for implementing prototypes and working with peer to peer frameworks. Experience with distributed systems or Web3 architectures is beneficial. The student should be comfortable with mathematical reasoning, protocol analysis, and hands‑on development in decentralized environments.

Research area: I specialise in making satellite radar imagery useful for the real world. My research develops AI-driven signal processing pipelines that clean, interpret, and extract meaning from Synthetic Aperture Radar (SAR) data — imagery that sees through clouds, smoke, and darkness where optical satellites fail. Using deep learning, computer vision, and classical ML, I transform raw radar signals into actionable maps of flooded regions, deforested areas, and land-cover change. My award-winning DFT-based speckle reduction framework, validated across multiple satellite frequencies, forms the methodological foundation of this applied remote sensing research.

Student roles:
The intern will work as an active research contributor with direct ownership of a meaningful public health problem. The role is structured across three progressive phases designed to build skills and confidence while delivering tangible research outcomes within the 12 week internship.
Phase 1 (Weeks 1 to 4): Onboarding and Data Preparation. The intern will begin by building foundational knowledge of atmospheric remote sensing and air quality science through guided reading and hands-on exploration. They will download and preprocess Sentinel-5P satellite datasets alongside ground-based air quality measurements from Environment and Climate Change Canada and meteorological data from open weather APIs. This phase ends with a clean, merged, analysis-ready dataset spanning multiple Canadian cities and multiple seasons.
Phase 2 (Weeks 5 to 9): Feature Engineering and Model Development. The intern will engineer meaningful features capturing pollution patterns across time, space, and weather conditions. They will train and evaluate machine learning models including Random Forest and Gradient Boosting to predict air quality index values at locations where no ground sensors exist. Model performance will be validated against held-out ground station measurements to ensure predictions are accurate and reliable. Deep learning approaches will be explored where time permits.
Phase 3 (Weeks 10 to 12): Visualisation, Interpretation, and Writing. The intern will generate interactive maps and visualisations showing predicted air quality hotspots across Canadian cities, interpret findings in the context of public health and urban planning, and contribute directly to drafting a peer-reviewed journal paper.
Throughout the internship the student will attend weekly one-on-one meetings with the supervising professor, maintain well-documented code in a shared GitHub repository, and deliver a final research presentation summarising their findings. The role is designed to give the intern complete ownership of a meaningful research workflow from raw satellite data to co-authored publication.

Skills required:
The ideal candidate is a senior undergraduate student in Computer Science, Software Engineering, Data Science, or Environmental Engineering with working knowledge of Python and a basic understanding of machine learning concepts. Familiarity with data analysis or statistics is an asset but not required. No prior experience with satellite data is necessary as the project is structured to onboard students from the ground up. Curiosity about environmental health, urban sustainability, and willingness to learn new tools are equally important. All software and datasets used are free and open-source.

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 actively participate in the development and evaluation of machine learning models to predict Urban Heat Island intensity. This role is designed to provide hands-on experience in applying data science and machine learning to environmental monitoring and urban analysis. Key responsibilities will include:

Learning and Background Research: Developing an understanding of the Urban Heat Island effect, the types of data used (satellite imagery, sensor data, meteorological data), and the fundamentals of machine learning algorithms suitable for prediction.
Data Management: Assisting in gathering, organizing, and cleaning diverse datasets. This involves tasks like handling missing data, merging data from different sources, and preparing data formats suitable for machine learning models.
Feature Engineering Assistance: Participating in discussions and assisting in the process of selecting relevant input variables (features) and potentially creating new features from the raw data to improve model performance.
Model Implementation and Training: Implementing selected machine learning models using programming tools (primarily Python with libraries like Scikit-learn, possibly Keras/TensorFlow). Assisting in running experiments to train these models on the prepared data.
Evaluation and Analysis Support: Helping to assess the predictive accuracy of the trained models using appropriate metrics. Contributing to the comparison of different models or feature sets and interpreting the results.
Visualization: Assisting in creating plots, charts, or potentially maps to visualize the input data, UHI patterns, or the model predictions, helping to communicate the project findings.
Documentation: Contributing to the documentation of the project, including descriptions of data sources, methodologies used, code implementation, and key results.

Skills required:
Solid programming skills, particularly in Python, are essential for data processing and implementing machine learning models. A good understanding of fundamental machine learning concepts (e.g., supervised learning, regression, classification, model validation) and basic statistics is required. Experience or willingness to learn data handling and preprocessing techniques for diverse datasets (tabular, potentially time-series or spatial) is important. Familiarity with common Python data science libraries (like Pandas, Scikit-learn) is advantageous. Mathematical aptitude is necessary. An interest in environmental science, urban studies, or data visualization would be beneficial, coupled with enthusiasm for applying ML to real-world environmental challenges

Research area: - Statistical signal processing - Forecasting and Estimation

Student roles:
The student will be involved in developing and testing forecasting algorithms and connecting them to our web platform. The project has both a theoretical component (understanding and extending the probabilistic models) and a practical one (implementation and validation on real sport data).

Skills required:
- Background: computer science/engineering
- Programming : python
- Autonomy : self-learning capacity