Engg-Computer

Research area: Astronomical observations reveal a universe populated by an amazingly diverse array of structures, including our own Milky Way galaxy. How did these form? How did they acquire their observed properties? How do supermassive black holes impact the evolution of the galaxies? I investigate these questions by using powerful supercomputers to model the evolution of the universe from the Big Bang to today.

Student roles:
The role of the student will depend strongly on the student's background and skill sets. In all instances, the student intern will work closely with one or more members of my research group. Invariably, the student will be involved in developing and/or refining Python code and scripts; running simulations on high performance supercomputers; analyzing the simulation outputs -- a process that includes making high-quality plots and graphics as well as movies. The intern will be expected to provide at least once-a-week updates to the research group. This is an excellent opportunity to learn and hone presentation skills. And, if the project evolves sufficiently, the intern will participate in drafting a manuscript for publication.

Skills required:
This project is at the intersection of astrophysics and data science. A suitable intern is expected to be a motivated, independent researcher, a go-getter who is willing to learn new techniques, investigate new directions, think out-of-the-box, and be persistent. The intern will work closely with my research team. Given the nature of the research project excellent programming (and debugging) skills are essential. Familiarity with Python would be an asset. Students interested in working on the AI/ML aspect of the project should have familiarity and background in building/training networks as well as different forms of network architectures.

Research area: My research focuses on smart grids and hybrid AC/DC microgrids, with emphasis on energy management systems (EMS), optimization, and integration of renewable energy resources, electric vehicles, and energy storage. I develop advanced optimization and data-driven methods to improve the efficiency, reliability, and resilience of modern power systems. My work also includes cyber-physical security, uncertainty modeling, and real-time operation of microgrids.

Student roles:
The student will contribute to the development of an AI-assisted framework for resilient microgrid operation. Tasks include modeling different outage scenarios, generating simulation datasets using an existing energy management system, and developing machine learning models to predict system responses or assist optimization.

The student will implement and test algorithms in MATLAB or Python, analyze system performance under different operating conditions, and compare AI-assisted approaches with conventional optimization methods. The student will also participate in interpreting results, preparing figures, and contributing to technical reports or publications.

Skills required:
Background in electrical engineering, power systems, or energy systems. Knowledge of optimization (e.g., linear programming) and basic machine learning is an asset. Experience with MATLAB or Python is required. Familiarity with power system analysis, microgrids, or renewable energy systems is desirable. Strong analytical skills and ability to work independently are important.

Research area: Prof. Maha Ben Ali is a professor in industrial engineering whose research focuses on artificial intelligence and data-driven decision-making for demand-driven production systems and sustainable supply chains. Her work centers on developing AI-powered decision-support systems that align production and sales decisions in complex industrial environments. She combines optimization, machine learning, and advanced analytics, and maintains strong collaborations with industry to ensure real-world impact and applicability.

Student roles:
Working in close collaboration with an industrial partner, the student will contribute to the design and implementation of artificial intelligence models aimed at optimizing sales decisions under uncertainty. More specifically, the student will:
-Analyze real industrial data, including inventory levels, sales transactions, and market trends;
-Develop and implement AI and optimization models to support order commitment decisions;
-Model buyer behavior to improve pricing and sales strategies;
-Contribute to the integration of data from multiple sources (inventory, transportation, client profiles, etc.) into decision-making frameworks;
-Test and validate proposed models using real-world industrial scenarios;
-Collaborate with partner stakeholders to ensure the practical relevance and applicability of the solutions;
-Participate in the development of an innovative software module for industrial deployment;
-Document findings and present results through technical and scientific reports;
Through this project, the student will gain hands-on experience in applied artificial intelligence, industrial data analytics, and decision-making under uncertainty in a real manufacturing context.

Skills required:
Strong modeling and data analysis skills using tools such as Python, R, or equivalent
Familiarity with information systems used in manufacturing environments (MRP, ERP, SAP, etc.)
Knowledge of simulation and optimization tools is an asset
Ability to write scientific or technical reports in English or French
Strong interest in working on real-world industrial problems in collaboration with an industry partner

Research area: • Resource and Mobility management in future wireless networks (5G/6G networks, O-RAN). • Cybersecurity in 5G/6G/O-RAN networks. • AI and Quantum AI for wireless networks. • Green networking and Green Cloud. • Computation Offloading in Mobile Edge Computing (MEC). • Smart cities and Software defined wireless networks. • Network Digital Twin.

Student roles:
Le projet se déroulera en plusieurs étapes complémentaires :

1) État de l'Art
L’étudiant réalisera une revue approfondie de la littérature portant sur les modulations LR-FHSS (DR8/DR9) et LoRa (SF7-SF12), en analysant leurs performances, l’architecture LoRaWAN et les contraintes des communications hybrides terrestre-satellite. Une attention particulière sera accordée aux caractéristiques des constellations LEO, notamment les effets du Doppler, les angles d’élévation et les fenêtres de visibilité.

2) Génération Dataset par Simulations Exhaustives
L'étudiant paramètrera le simulateur existant pour exécuter 1000+ simulations systématiques couvrant une grille complète de scénarios : élévations satellite [5°,15°,30°,45°,60°], vitesses relatives [0, 7.8km/s, 15km/s], et toutes modulations cibles [LR-FHSS DR8/DR9, LoRa SF7 à SF12]. Chaque simulation génèrera un triplet structuré (position_satellite, modulation, PER_% ) stocké au format CSV avec métadonnées (RSSI, SNR, fenêtre de visibilité). Le dataset final permettra d'entraîner l'agent IA à prédire le PER pour toute combinaison géométrie/modulation non encore simulée, assurant une base fiable pour les décisions adaptatives.

3) Prompts Spécialisés
L’étudiant développera ensuite des prompts spécialisés intégrant une approche RAG (Retrieval-Augmented Generation). Ces prompts permettront d’analyser les conditions du canal, d’identifier des scénarios similaires dans le jeu de données, d’estimer les performances des différentes modulations et de recommander dynamiquement la configuration la plus adaptée. Plusieurs stratégies d’optimisation des prompts seront évaluées afin d’améliorer la qualité des décisions.

4) L’agent IA sera ensuite évalué selon plusieurs indicateurs de performance, notamment la précision des décisions, le temps de réponse, la robustesse dans les conditions difficiles, la réduction du taux d’erreur paquet et la limitation des hallucinations.

5) Enfin, une interface interactive sera développée pour visualiser en temps réel les paramètres satellites, les prédictions de l’agent, les modulations recommandées ainsi que l’historique des décisions, tout en permettant une intégration directe avec le simulateur via des commandes automatisées.

Skills required:
- Des compétences informatiques pratiques sont indispensables pour la mise en œuvre de l’agent IA
- Être à l’aise avec la manipulation des algorithmes de Machine Learning, des datasets, etc.
- Maîtriser le langage Python
- Avoir de bonnes connaissances sur les réseaux mobiles, NTN (Non Territorial Network), Satellite-IoT, le contrôle d’accès, les technique de modulation LoRa est un grand plus.

Research area: software performance engineering cloud computing internet of things Machine Learning Artificial Intelligence

Student roles:
The student will work in a team of Masters, PhDs, Postdoctoral and undergraduate students
The students will learn and develop collect data, train and test a variety of LLM models
Ideally, the work the student create will be published in posters, conference and journal papers.

Skills required:
-Programming in one of the common languages, such as Java, JS, Python, Ansible
-Working knowledge of cloud native platforms such as Kubernetes, Docker
-Optional: Knowledge of Agentic AI frameworks such as Langgraph or Langchain and/or some foundation models

Research area: It is vital to moving AI from theory to practice, solving real issues using practical technology and methods. HealthTech represents one of the most promising opportunities for applying AI to improve quality of life, healthcare delivery, and community well-being. We aim to develop diagnostic tools and Smart digital health monitoring systems by applying AI, LLM techniques to improve disease diagnosis. As an expert in translational medicine and applied machine learning (ML) to developed ~20 ML models and healthcare pipeline for clinical research laboratories including stroke risk predictor, heart attack risk stratification, Diabetes etc. I authored 60+ journals, 150+ conferences.

Student roles:
The student will play a central role in adapting an existing healthcare hardware platform, originally designed for wheelchair users, into an elderly healthcare monitoring system focused on medication adherence, daily interaction, and behavioral change monitoring. The student will help design and implement the core A2A monitoring workflow, including data capture from the hardware system, reminder and logging functions, and the communication pipeline that detects concerning changes and escalates alerts to a clinician-facing endpoint. They will also contribute to developing a practical and user-friendly prototype that demonstrates how the system can support elderly care in a realistic healthcare setting.

In addition, the student will participate in testing and refining the proof-of-concept by analyzing monitoring data, evaluating system behavior, and improving the reliability of the escalation process. This includes helping assess how well the different components of the system work together and identifying areas for improvement in usability, monitoring logic, and communication flow.

Through this project, the student will gain hands-on research training in intelligent healthcare systems, agent-to-agent communication, health data monitoring, and prototype development. They will strengthen technical skills in AI-enabled healthcare applications, system integration, data analysis, and experimental evaluation, while also developing soft skills through regular mentoring, technical discussions, research presentations, and scientific writing. The student will also gain valuable exposure to interdisciplinary health research, preparing them for future opportunities in academia, industry, or digital health innovation.

Skills required:
Basic background in Large Language Models (AI).
Basic knowledge in Intelligent Agents skills
Basic knowledge in Clinical Design Making
Strong programming skills (preferred python).
Basic knowledge about health monitoring systems.
Basic knowledge about source code management (like Git protocol).

Research area: My research focuses on safety assurance and governance of AI in autonomous and safety-critical software systems. I work on how machine learning components, increasingly including large language models, can be justified for deployment in regulated environments: structured assurance arguments, safety-case methodologies, evidence generation, and risk-based AI lifecycle governance aligned with NIST AI RMF and ISO/IEC 42001. My broader interests span agentic AI, AI security, and responsible AI operationalization. Combining enterprise AI leadership with academic research, I supervise projects that connect formal assurance methods to the practical realities of deploying AI in high-stakes systems.

Student roles:
The intern will lead both the assurance-case construction and the empirical evaluation.
Weeks 1–4: review GSN, AMLAS, and LLM evaluation literature with supervisor guidance; define the reference scenarios and hazard analyses.
Weeks 5–8: instantiate the full assurance cases and design empirical probes for testable evidence claims.
Weeks 9–12: execute the probes, classify all evidence obligations into the satisfiability taxonomy, finalize the pattern catalogue, and co-author the paper draft.
The student will participate in weekly supervision meetings, maintain a structured research log and version-controlled artifacts, and deliver a final presentation.

Skills required:
Strong analytical and technical writing skills. This project combines structured argumentation with empirical work. Solid Python programming for implementing evaluation probes (robustness, calibration, consistency testing) on language models. Foundations in software engineering or systems thinking from coursework; exposure to safety engineering, formal methods, or requirements engineering is an asset but not required. Familiarity with large language models and their failure modes is helpful and will be deepened during the internship. Rigor, precision, and interest in how high-stakes industries justify trust in AI systems are essential.

Research area: My research is in human-computer interaction, information visualization and mobile user interfaces. I am interested in designing better interfaces for Augmented Reality and Virtual Reality systems.

Student roles:
The student will work as part of a research team investigating new user interfaces for semi-autonomous and self-driving vehicles. Using a Unity 3D-based driving simulator, the student will help design, develop, and evaluate novel interaction techniques that leverage augmented reality (AR), virtual reality (VR), and other emerging technologies to improve the driving experience.

Depending on their interests and background, the student may contribute to the implementation of interactive prototypes, development of visualization and feedback mechanisms, integration of AR/VR components, and the creation of experimental scenarios within the driving simulator. The student will also assist with planning and conducting user studies, collecting and analyzing data, and interpreting results to inform future interface designs.

The project provides opportunities to explore a range of research topics, including driver awareness, trust in autonomous systems, transitions between manual and automated control, immersive training environments, and AI-assisted interactions. Students will collaborate with faculty members and graduate researchers while gaining experience in user-centered design, software development, experimental research methods, and data analysis.

Successful students will be expected to contribute ideas, participate in team discussions, document their work, and help advance one or more components of a larger research program focused on the future of human-vehicle interaction. This role offers valuable experience at the intersection of Human-Computer Interaction, immersive technologies, visualization, and intelligent transportation systems.

Skills required:
We welcome students from computer science, engineering, human-computer interaction, game development, psychology, or related disciplines. Candidates should have an interest in interactive technologies, user experience design, autonomous vehicles, or immersive systems. Experience with programming is required, and familiarity with Unity 3D, C#, virtual or augmented reality development, or user interface design is considered a strong asset. Students should be comfortable learning new technologies, working independently and collaboratively, and contributing to research activities such as prototyping, user studies, and data analysis. Strong problem-solving and communication skills are important for success in this interdisciplinary project.

Research area: Prof. Maha Ben Ali is a professor in industrial engineering whose research focuses on artificial intelligence and data-driven decision-making for demand-driven production systems and sustainable supply chains. Her work centers on developing AI-powered decision-support systems that align production and sales decisions in complex industrial environments. She combines optimization, machine learning, and advanced analytics, and maintains strong collaborations with industry to ensure real-world impact and applicability.

Student roles:
The student will contribute to developing AI and optimization models for lignin valorization processes. Tasks include data collection, literature review, model development, scenario analysis, and evaluation of sustainability and performance metrics. The student will collaborate with multidisciplinary researchers, participate in technical discussions, and contribute to reports, presentations, and scientific publications. The internship will provide hands-on experience at the intersection of AI, sustainability, and industrial innovation.

Skills required:
Experience in programming, data analysis, machine learning, or process modeling is considered an asset.
Knowledge of biomass conversion, bioprocessing, or sustainability assessment is desirable but not required.
Applicants with strong analytical skills and a keen interest in sustainable technologies are strongly encouraged to apply.
Excellent scientific and technical writing skills in English or French.
Strong interest in developing innovative solutions to real-world industrial challenges through close collaboration with industry partners.

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:
During the project, the student will meet weekly with the supervisor and participate regularly in group research discussions. The student’s role and a tentative 12-week schedule are outlined below:
• [Weeks 1–3]: Review TTCP CAGE Challenge 4 and related research, including four to five papers on MARL, as well as the project team’s prior research on MAPPO and LLM-based agents for cyber defence.
• [Weeks 2–6]: Set up the Cyber Operations Research Gym (CybORG) and the CAGE Challenge 4 environment. Test the preliminary MAPPO solution and study other MARL methods, such as QMIX and graph-based MARL.
• [Weeks 4–8]: Collaborate with the supervisor and project team to develop ideas for enhancing the MAPPO solution and explore an alternative MARL solution. Conceptually compare the two solutions, analyze their advantages and limitations, and position both relative to existing solutions in the literature reviewed in Weeks 1–3.
• [Weeks 6–11]: Implement and test the enhanced MAPPO solution and the alternative MARL solution in the CybORG environment. Evaluate and compare their performance, analyze the results, and iteratively refine the proposed solutions.
• [Week 12]: Prepare a technical report summarizing the research findings from the 12-week project.

Skills required:
Network Security: The student should have taken at least one course in cybersecurity or network security and understand basic concepts such as threats and attacks, authentication, access control, and intrusion detection. Prior research experience in network security is preferred but not required.

Machine Learning: The student should understand the basics of machine learning, including supervised learning, unsupervised learning, and, most importantly, reinforcement learning. Hands-on experience with ML libraries and programming is preferred. Prior knowledge of MARL is an asset but not required.

Programming Skills: The student should be proficient in Python.

Research area: My specialized research area focuses on the intersection of mechanical engineering and advanced imaging systems for diverse applications in engineering and science, including livestock health assessment, biomechanics, and biomedical devices. This interdisciplinary work integrates principles of mechanical engineering, optical diagnostics, and machine learning to design and implement high-performance camera-based systems capable of capturing and analyzing complex visual data in real time.

Student roles:
1. Machine Learning and Image Analysis:
o Develop and train computer vision models to detect qualitative traits and signs of disease (e.g., mastitis) in livestock images.
o Preprocess and annotate image datasets, evaluate model performance, and refine algorithms.

2. Imaging System Development:
o Assist in the design, assembly, and testing of camera-based imaging hardware suitable for farm environments.
o Support integration of imaging components with software for real-time data acquisition.

3. Data Collection and Management:
o Participate in field data collection, including image capture and annotation.
o Organize, clean, and maintain datasets for training and testing purposes.

4. Documentation and Communication:
o Document technical work, experimental procedures, and research outcomes.
o Participate in team meetings, provide progress updates, and contribute to final reports or presentations.

Skills required:
-Proficiency in Python (or similar language)
-Experience with machine learning frameworks such as TensorFlow or PyTorch
-Understanding of computer vision techniques (e.g., image classification, object detection)
-Familiarity with camera systems, optical components, and image processing
-Experience using tools such as OpenCV for image analysis is a plus
-Mechanical or Systems Engineering (optional but valuable):
-Skills in hardware prototyping, sensor integration, or system design
-Ability to manage and preprocess large datasets
-Attention to detail for image labeling and quality control
-Strong problem-solving and critical thinking abilities
-Ability to work independently and collaboratively in a research setting
-Good communication skills for documentation

Research area: Autonomous driving holds rosy promises. However, stand-alone vehicles can hardly beat an experienced human driver with social learning skills. Alternatively, vehicles can exchange information using low-cost wireless technologies. With abundant information from other vehicles, pedestrians, road-side infrastructure and clouds, each connected vehicle (new or even old one) can see and sense much better than any individual driver or car, and make more intelligent decisions to ensure safety, improve transportation and energy efficiency, reduce emission, and provide infotainment services. Vehicle-to-everything (V2X) communications combined with Large Autonomous Driving Models are thus critical for future intelligent transportation systems.

Student roles:
The MITACS supported students are mainly to support the research team in developing connected autonomous driving technologies. They will first use two weeks to understand the current research project and activities in our team. Then, in the following four weeks, they will learn the current experiment environment, and then develop new implementations of the communication protocols and control strategies used in the platform. The last two weeks will be dedicated in documenting and summarizing the research findings, and writing reports and possibly publications from the research findings.

Skills required:
The students should have an academic background from one of the following disciplines: computer engineering, electrical engineering, software engineering, or computer science. They should have some programming skills in C/C++ or Python. They should have the basic knowledge in communication networks and machine learning.

Research area: Le domaine de recherche est le traitement de données acoustiques sous-marines dans un but de conservation des espèces marines en voie de disparition comme le béluga du Saint-Laurent. Nous développons des approches statistiques et des modèles d'intelligence artificielle pour le traitement des données.

Student roles:
- création d'une base de données synthétique
- entraînement d'un CNN existant sur la base de données créée (Python)
- test et évaluation

Skills required:
L'étudiant doit être familier avec la programmation Python, le deep learning et l'intelligence artificielle en général. Des connaissances en acoustique sont un atout.

Research area: Broadly speaking, our research interests lie in the diverse area of mathematical imaging and inverse problems. Our long-term research objective is directed towards developing and validating efficient numerical methodologies for solving real-world, ill-posed inverse problems in the field of medical image processing. The focus of this project is on deep learning techniques for medical image resolution enhancement.

Student roles:
The potential candidate will be responsible for utilizing and extending our image resolution enhancement tools and algorithms in Python.

Skills required:
The potential student is required to have excellent programming skills in Python. The candidate may have background in different disciplines including computer science, applied mathematics, physics, electrical and computer engineering, biomedical engineering, or a related field. Excellent knowledge of calculus and linear algebra is also required. Experience working with medical imaging data and numerical optimization schemes is desirable but not required.

Research area: I'm an Associate 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;
- Implement in Python an experimental setting to train and test different deep learning configurations using data sets with a different imbalance ratio.
- Run the experiments in Python and collect and carry out an analysis of the results;
- Write a report that describes the experiences carried out and 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 deep learning.
To work in this project the student should have:
- Basic knowledge and experience with machine learning algorithms (preference will be given to student with prior expertise in deep learning)
- Experience setting up and running experiments in Python
- Good written and communication skills.

Research area: Human activities are linked to mental and physical functions. Critical applications require assurance of human activities to meet target performance and safety measures. The monitoring of human activities is difficult and might not be possible in some situations where brain functions might not be visible and can only be understood by understanding human behavior. The use of wearable technologies for health and brain monitoring will enable the understanding of brain functions via the analysis of signals from the human monitoring system. There are difficulties of analyzing bio signals due to complexity of signals without proper understanding of other behavior

Student roles:
T1. Selection and analysis of human activities in critical applications
T2. Model of human behavior in each activity
T3. Evaluation and selection of suitable human health and performance monitoring technologies with monitoring points
T4. Data collection from different sensors for different activities
T5. Analysis of collected input data and mapping to factors from human behavior and activities
T6. Assess human performance measures using input data, with risk factors
T7. Design and demonstration of the integrated system for selected application

Skills required:
The proposed solution includes human health and performance monitoring system, connected to computational server, and human behavior knowledgebase that includes human activity semantic network (HASN). Human performance measures are modeled and linked to human activities, and factors that impact each activity. Physical system components are modeled and linked with human activities and related behavior.

Research area: My research specializes in advancing the design and autonomous capabilities of small spacecraft through a systems-level approach that integrates reliable power systems and onboard machine learning. I focus on developing and applying rigorous design methodologies to ensure scalability, efficiency, and resilience across all subsystems. This includes model-based design, hardware-in-the-loop testing, and modular components that support rapid prototyping and deployment. By addressing both hardware and software challenges, my work aims to enable next-generation small satellites capable of autonomous operation to support the New Space.

Student roles:
Under the guidance of faculty and research mentors, the student will:
Conduct a literature review on LoRaWAN protocol adaptations for space applications, low-power communication strategies, and existing satellite-IoT systems.

Assist in the design and simulation of satellite communication links, considering factors such as link budget, Doppler effects, and coverage.

Support the implementation and testing of LoRa-based communication modules using software-defined radios (SDRs) or hardware development boards.

Contribute to the integration of the communication system with the onboard computer, enabling the data transmission and reception.

Participate in testing and validation through simulations and potentially hardware-in-the-loop environments.

Document development activities, summarize findings, and support the preparation of reports or research publications as appropriate.

Skills required:
Background in electrical engineering, computer engineering, or a related field, with a strong interest in satellite communications and IoT technologies.
Fundamental knowledge of wireless communication systems, including modulation, signal processing, and link budgeting.
Experience or familiarity with LoRa/LoRaWAN protocols and their application in low-power, long-range communication scenarios.
Proficiency in programming (e.g., C, C++, Python), particularly for embedded systems and communication protocol implementation.
Exposure to or hands-on experience with software-defined radios (SDRs), RF testing tools, or communication hardware platforms is highly desirable.
Strong analytical skills and the ability to conduct literature reviews, perform simulations, and evaluate communication system performance.

Research area: Prof. Maha Ben Ali is a professor in industrial engineering whose research focuses on artificial intelligence and data-driven decision-making for demand-driven production systems and sustainable supply chains. Her work centers on developing AI-powered decision-support systems that align production and sales decisions in complex industrial environments. She combines optimization, machine learning, and advanced analytics, and maintains strong collaborations with industry to ensure real-world impact and applicability.

Student roles:
Model the lumber production process.
Identify and evaluate the technologies required for real-time data collection and integration.
Develop machine learning models to forecast flows, analyze deviations between planned and actual operations, and recommend corrective adjustments.

Skills required:
Strong modeling and data analysis skills using tools such as Python, R, or equivalent Knowledge of information systems used in manufacturing environments (MRP, ERP, SAP, etc.) is an asset
Knowledge of simulation and optimization tools is an asset
Ability to write scientific or technical reports in English or French
Strong interest in working on real-world industrial problems in collaboration with an industry partner

Research area: Our research group specializes in the design, fabrication and testing of specialty optical fibers and photonic devices for applications in optical communications and optical sensing. The domains of research and applications also extend to materials sciences, biomedical sensors and quantum technologies.

Student roles:
The intern will learn how to implement inverse-design algorithms for the specific task of optimization of optical fiber designs. The intern will report to the supervisor by means of in-person meetings (1/week), written weekly reports and technical reports.

Skills required:
- Experience and proficiency with programming (e.g., Python, Matlab)
- Basic knowledge of wave optics & fiber optics
- Good communication skills
- Ability to work inside a team

Research area: Dr. Gül’s research interests lie in the area of smart, sustainable and resilient cities with a focus on two main areas: • Developing smart, sustainable and resilient infrastructure systems; o Crowdsensing-based Monitoring of Built and Natural Environments (CoMBiNE) o Artificial Intelligence (AI) for CoMBiNE o Digital Signal & Image Processing for CoMBiNE o Data Analytics for Sustainable, Smart and Resilient Infrastructure Systems • Developing smart, sustainable and Energy-Efficient Communities & cities; o Solar PV Systems and their Integration to Energy-efficient Buildings o Community/City-wide Solar PV Applications o Energy Efficient Smart Buildings and Net-zero/Net-plus Energy Homes o IoT Applications for Energy-Efficient

Student roles:
The student will work closely with the other team members to work on exciting and innovative projects to develop crowdsensing technologies for smart cities. The role of the summer student includes but not limited to: Signal Processing, Image Processing, Deep Learning Applications using CNNs, RL, IoT, and Data Analytics. The student will also help develop manuscripts and reports.

Skills required:
Requirements:
1. Background in engineering or computer science.
2. Experience in AI/deep learning, image/video processing, data analytics, signal processing
3. Programming skills and familiarity with related software and programming languages, especially Python and MATLAB
4. Data analytics and management skills and ability to develop databases
5. Strong written and oral communication skills.
6. Excellent organizational skills.

Research area: Our Standard Model only can explain 5% of the energy in the universe while 95% remain a mystery. Neutrinos may hold a key to discovering new physics.With next-generation Xe experiments we will be searching for neutrinoless double-beta decays in Xe-136, which are forbidden in the Standard Model since lepton number conservation is violated. An observation of this decay would require the neutrino and anti-neutrino to be identical particles, unlike all other known particles. I am involved in the development or new technologies for the future XLZD detector which has a projected sensitivity close to 10^28 years..

Student roles:
You will be working independently on the development of part of the Ba-tagging system. The whole system is currently being upgraded and improved so there is plenty of work. Depending on your interests, you will be involved in simulating and designing ion-manipulation techniques such as ion optics and ion guides, or the assembly and testing of individual components of the Ba-tagging setup. A LabVIEW control system has to be developed to monitor the individual operation parameters of the system and log their values. You can rely on members of the local group at McGill to support you and to help you with your project. We will to answer your questions and we are looking forward to discussing your ideas.

Skills required:
A general background of physics would be helpful in understanding the concepts of our setup. However, an interested and motivated student will pick up the details while working on the project. A general understanding of computers will be helpful. SIMION is a powerful ion trajectory simulation toolkit with a gui that is very intuitive. For the lab work, excitement and motivation are all you need. We will instruct you in how to assembel ultra-high vacuum systems and design experiments.

Research area: Dr. Tasnim is an Associate professor in the Mechanical Engineering program in the School of Engineering, at the University of Guelph. Dr. Tasnim is currently working on two projects funded by the Egg Farmers of Canada. The first project involves “measuring dust and greenhouse gases in layer houses in Ontario” and the second one is “to design alternate ventilation designs for layer houses”. Dr. Tasnim is currently working on the development of low-cost sensors for poultry barns. Therefore, she has the required knowledge and expertise to successfully complete the project.

Student roles:
The students will first learn the operating principles of the low-cost environmental sensors used in this project. These sensors include measurements of temperature, relative humidity, static pressure, particulate matter (PM2.5 and PM10), CO₂, NH₃, and CH₄. The students will then integrate these sensors with an Arduino-based microcontroller system for data acquisition. As part of the system setup, the students will program and modify the data collection frequency and test the integrated sensor system in a controlled laboratory environment to verify proper functionality and data recording.

Following the initial testing, the students will conduct sensor calibration by comparing the measurements obtained from the low-cost sensors with data from reference instruments that provide accurate and validated readings. Finally, the students will deploy the sensor system in commercial poultry barns and perform field testing alongside the reference device to develop calibration curves and evaluate the performance of the low-cost sensors under real operating conditions.

Skills required:
Third- or fourth-year computer, mechanical, or environmental engineering students with a background of basic engineering courses will be preferred. Experience working with different types of sensors and microcontrollers and data collection is preferred. Important skills:

Integrating sensors with microcontrollers
Sensor calibration
Comparison with reference instruments
Error analysis and uncertainty estimation
Statistical analysis

Research area: Energy use in industries can be reduced by 5-20% or more by identifying and correcting abnormal situations. Monitoring processes for failure detection is becoming an issue to maintain the competitiveness of the manufacturing sector. Industry owners have access to a variety of government and private programs, incentives to reduce energy consumption including adherence to best practices, elimination of inefficiencies, reduction of production costs, energy reuse. produced and reduced carbon emissions.

Student roles:
1. Fine-tune LLM, VLM. The establishment of an intelligent system including an expert system using prediction algorithms fully serving the control and supervision of a targeted industrial process, affecting the entire production chain. The objective is to establish and design a system that will allow rapid action to be taken on issues directly related to the process, while maintaining a tight flow.
2. Increase the decision-making speed of the actions to be taken while respecting the safety aspect in the control and supervision of the process.
3. Improve the electronic instrumentation and measurement platform.
4. The implementation under XCos, ROS and on an FPGA.

Skills required:
The student must master the concepts of real-time embedded programming as well as master some concepts in electronics. At least one candidate will work on artificial intelligence and one candidate will work on an FPGA.

Research area: My research work focuses on improving the security of computer systems, especially at the digital hardware design level. My work involves RTL/microarchitecture design, with RTL/gate-level simulation, as well as FPGA-based prototyping. I am also interested in exploring the implications of emerging machine learning techniques on the IC supply chain and system-on-chip life cycle. My research interests include embedded systems design and electronic design automation. I am also interested in understanding security-related topics in machine learning (both security of ML and applications of ML to security).

Student roles:
The student will design and implement original hardware modules across varying levels of complexity and architectural styles. For each module, they will produce clean and buggy variants. They will also write testbenches and formal properties. Documentation of the process is also part of the role.

Skills required:
The student should be a confident HDL designer. They need to implement correct, synthesizable modules from written specifications without referencing existing online code. Knowing how to simulate and verify their own designs is essential. Some familiarity with Assertion-Based Verification (ABV), such as SystemVerilog assertions (SVA), is a plus.

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 to address complex challenges such as trajectory optimization, digital twins for urban mobility, map-matching, and indoor-outdoor navigation. My research aims to develop innovative solutions for urban mobility and make a significant contribution to creating smarter, more resilient urban environments.

Student roles:
The student will play a key role in designing and developing a comprehensive Digital Twin (DT) framework for urban mobility, with a specific focus on integrating a scalable Estimated Time of Arrival (ETA) prediction engine into a multimodal transport simulation environment. Their primary responsibilities will include conducting an in-depth literature review on spatiotemporal modelling techniques, graph-based neural network architectures, and transfer-learning strategies for cross-city generalization. They will identify relevant real-time and historical data sources—such as traffic sensor feeds, public transit schedules, and shared-mobility usage logs—and establish data ingestion pipelines using Python and cloud-based services to ensure data quality, consistency, and scalability.

Technically, the student will be responsible for designing, implementing, and training the core ETA prediction module. This involves building graph neural networks augmented with attention mechanisms to capture spatial interdependencies (e.g., network topology and congestion spillover) and temporal dynamics (e.g., demand fluctuations and signal timing variations). They will incorporate a hierarchical decomposition strategy to partition the urban transport graph into subregions, enabling parallelizable inference for real-time “what-if” scenario analysis. To ensure model transferability, the student will develop and validate a transfer-learning workflow that adapts pretrained models to new urban contexts with minimal data, experimenting with latent representation alignment and domain-adaptation techniques.

The student will also collaborate on integrating the ETA module into the broader DT simulation platform. They will help develop lightweight frontend components that allow planners to configure policy scenarios—such as adjusting signal timings or introducing new transit corridors—and visualize predicted ETA impacts across all modes. Throughout the project, they will document data schemas, modelling decisions, and code repositories, facilitating reproducibility and future extensions. Additionally, the student will prepare regular progress reports, co-author academic publications, and present findings in project meetings with faculty and collaborators.

Skills required:
Academic background in Computer Science, Applied Computing, Data Science, Electrical and Computer Engineering, or equivalent. Demonstrated expertise in machine learning, deep learning, and data analytics, including proficiency in Python and frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with cloud-based environments (e.g., AWS, Azure, GCP) and familiarity with frontend technologies is advantageous. Interest or prior exposure to Intelligent Transportation Systems (ITS), connected and autonomous vehicles, and urban mobility challenges is desirable. Strong analytical and problem-solving skills, with the ability to conduct independent literature reviews, synthesize findings, and communicate technical concepts. Excellent collaboration and strong time-management capabilities.

Research area: My research is situated under the broader umbrella of systems engineering. Digital twins are real-time computational reflections of physical systems and allow for controlling physical systems for optimal behavior. In this line of research, we published extensively on engineering digital twins, e.g., inferring simulators in digital twins by reinforcement learning, with applications at the largest indoor food producer in the world. We conduct leading research on the engineering of digital twins, including their modeling, simulation, and architectural concerns. I maintain additional lines of research on artificial intelligence, machine learning, and modeling&simulation.

Student roles:
You will work closely with the PI and potentially with graduate researchers. You will solve technical and scientific challenges within our research line focusing on digital twins. Most of your work will consist of discussing research and engineering problems within the team; planning out feasible solutions; implementing them; running experiments (e.g., performance evaluations); and discussing your advancement time by time.

We foster an egalitarian culture in our lab, which means that we treat you as a colleague: we work together, ideate together, and solve problems together. You and the PI will scope your internship in a way that is interesting and motivating for you, and useful for our lab. Ours is a particularly high-paced research environment that requires above-average drive, autonomy, and communication skills. Apply only if you consider yourself an excellent, motivated, and driven student and you truly want to excel in your profession.

Skills required:
This project requires a background in software engineering or computer science.

1. Rock-solid programming skills, particularly in Python.
2. Rock-solid understanding of the typical software engineering lifecycle. At minimum, you will need to be comfortable with using git/GitHub, Linux, and have to be familiar with basic DevOps.
3. Basic understanding of IoT concepts (sensors, data collection, etc).
4. Basic knowledge of Robot Operating System (ROS), programming a Raspberry Pi, etc.
5. Ability to read technical content (technical reports, documentation, scientific works).
6. Highly autonomous and goal-oriented personality. Excellent communication skills. Strong command of English.

Research area: Our research group has been involved in telepresence experiences for distributed musical practice and performance, and in development of a camera array architecture for remote viewing of cadaveric dissections for remote surgical training. We are now working toward combining these technologies in an augmented reality framework in which musicians will be able to collaborate with other performers in a manner in which the differences between co-located and remote are minimized.

Student roles:
Depending on interests, your tasks will include some of the following:

- updating audio plugins for digital audio workstation for lower latency experience
- calibrating visible light cameras for use with view synthesis software
- investigation of alternative real-time dynamic scene rendering algorithms
- refinement of live stream rendering architecture in conjunction with HoloLens2 and Varjo XR3 HMDs
- implement visual hull segmentation of musician from background for blending into AR display
- dynamic view perspective update based on user motion
- output user pose information from HMD to audio subsystem to drive spatial audio display effects

Skills required:
Excellent programming skills and systems-building experience are essential. Basic familiarity with computer vision and/or computer graphics would be highly desirable.

Research area: Algorithms for wireless networks, Wireless communications, Technologies for the next generations of wireless networks, Resource allocation, Energy efficiency

Student roles:
The student will perform the following tasks:
- Read research articles suggested by the supervisor related to the project's field.
- Design, with the help of the supervisor, an algorithmic solution for resource allocation based on machine learning.
- Evaluate the performance of the proposed solution by conducting several computer simulations.
- Write a report presenting the proposed solution and the results obtained.

Skills required:
Master one or more programming languages among: Python, Matlab, R, C++.
Master the basic principles of computer networks.
Possess good knowledge of algorithms.

Research area: Le présent projet s'inscrit principalement à la croisée de deux domaines : la géomatique et l'informatique, communément appelée géoinformatique. Plus précisément, nous visons la création de nouvelles approches de développement pour des solutions web géospatial. Ces approches doivent intégrer des connaissances et des compétences en cartographie et en interface utilisateur pour assembler différents composants logiciels géomatiques et informatiques pour la création d’applications web de cartographie et de navigation.

Student roles:
Dans le cadre de ce stage, l’étudiant sera amené à:
• Apprendre des notions liées à la diffusion de données géospatiales et de cartographie sur le web, comme les services web géospatiales WFS et WMS ;
• Étudier des solutions de diffusion cartographique open source comme GeoServer ou MapServer;
• Étudier des solutions open source de cartographie web pour la navigation et l’interaction comme Leaflet, OpenLayers, GeoMoose, MapBender;
• Apprendre à effectuer des requêtes transactionnelles sur la base de données PostreSQL à partir d’une application web ;
• Concevoir et développer l’infrastructure de diffusion web géospatiales (service web et cartographie)
• Concevoir et développer un module d’édition
• Concevoir et développer un outil de navigation informant des orientations à prendre lors de parcours ou vers des points d’intérêt avec intégration de la localisation.
• Effectuer des tests sur le terrain pour valider que le système informatique développé fonctionne avec la prise en compte de la localisation Produire un rapport final décrivant explicitement les démarches effectuées dans le cadre de ce travail.

Skills required:
• Connaissance de certains langages de programmation orienté web (Javascript, HTML, CSS).
• Connaissance en géomatique (Système d'information géographique/SIG, géoinformatique) est un plus.
• Connaissance des technologies GitHub et Docker est un plus.
• L'expérience dans les réalisations de stages techniques et/ou de projets en lien avec le projet sera considérée avec attention.
• Un attrait et un intérêt pour le sujet devront être clairement démontrés.

Research area: Mes recherches se concentrent sur l’application de l’intelligence artificielle à la sécurité et la santé au travail, avec une expertise en vision par ordinateur, traitement de signaux, fusion de données capteurs, apprentissage profond, et systèmes embarqués intelligents. J’explore des approches avancées telles que les modèles fondamentaux, l’apprentissage fédéré et la distillation pour concevoir des systèmes fiables, temps réel et robustes, capables d’évaluer les risques en environnement industriel ou médical.

Student roles:
L’étudiant commencera par une phase de recherche à distance pendant laquelle il ou elle réalisera une veille scientifique sur les systèmes de détection de postures, de gestes à risque et d’anomalies dans les équipements de protection (ex. : masques respiratoires). Il ou elle étudiera des jeux de données existants ou participera à la préparation d’un jeu de données à partir de vidéos ou images capturées dans des environnements contrôlés.

Par la suite, l’étudiant développera des modèles d’analyse d’images (pose estimation, segmentation, classification d’action) capables d’identifier des situations potentiellement dangereuses. Il testera différentes architectures et approches d’apprentissage supervisé ou auto-supervisé, selon la disponibilité des annotations.

La phase au Canada sera consacrée à l’expérimentation de systèmes temps réel sur de nouveaux jeux de données ou en environnement simulé (ou semi-réel). L’étudiant optimisera les performances, explorera la distillation ou l’apprentissage fédéré pour la généralisation et la légèreté du modèle, et participera à la mise en place d’un démonstrateur intégrant capteurs et interface de visualisation.

Enfin, l’étudiant contribuera à la rédaction d’un rapport ou d’un article scientifique décrivant la méthodologie, les résultats expérimentaux et les applications envisagées. Il ou elle participera à des rencontres régulières pour discuter des orientations et des choix techniques.

Skills required:
Le candidat devra maîtriser Python et avoir de bonnes bases en vision par ordinateur (pose estimation, action recognition, segmentation) et en apprentissage profond. Des compétences dans l’utilisation de bibliothèques comme OpenCV, MediaPipe, PyTorch ou TensorFlow sont attendues. Une familiarité avec les capteurs inertiels ou les données thermiques est un plus. Autonomie, rigueur et esprit d’analyse sont essentiels.

Research area: Area of Interest : • Resource and Mobility management in future wireless networks (5G/6G networks, O-RAN). • Cybersecurity in 5G/6G/O-RAN networks. • AI and Quantum AI for wireless networks. • Green networking and Green Cloud. • Computation Offloading in Mobile Edge Computing (MEC). • Smart cities and Software defined wireless networks. • Network Digital Twin.

Student roles:
Ci dessous les étapes du projets en détail:
Les étudiants suivent ces étapes dans un notebook Google Colab (Python + LangChain + OpenAI/HuggingFace).

1. Choix du sujet de l'agent : L'étudiant propose un domaine (ex. : "Agent pour conseils nutritionnels", "Assistant cybersécurité pour détection de vulnérabilités", "Bot d'aide à la programmation Python"). Fournir une base de docs (PDFs, textes) thématiques (5-10 docs, ~50 pages).
2. Vectorisation et Embeddings :
• Charger les documents (langchain.document_loaders).
• Découper en chunks (RecursiveCharacterTextSplitter).
• Générer des embeddings (OpenAIEmbeddings ou HuggingFace).
• Stocker dans un vector store (FAISS ou Chroma).
3. Ingénierie de Prompts :
• Créer des prompts few-shot ou chain-of-thought pour guider le LLM.
• Tester l'amélioration (ex. : "Explique étape par étape en utilisant le contexte fourni").
4. Construction du RAG de base :
• Retrieval : Récupérer top-k docs similaires via similarité cosinus.
• Augmentation : Injecter le contexte dans le prompt.
• Génération : Utiliser un LLM (ChatOpenAI) pour répondre.
5. Enrichissement du RAG (avancé) :
• Hybrid search (keyword + semantic).
• Contexte multi-source (web + docs locaux).
• Gestion des hallucinations (vérification de faits).
6. Développement de l'Agent Intelligent avec LangChain :
• Utiliser LangGraph ou create_react_agent pour un agent avec tools (ex. : recherche web, calculatrice).
• Ajouter mémoire (ConversationBufferMemory).
• Déployer une interface (Streamlit ou Gradio).

Skills required:
- Des compétences informatiques pratiques sont indispensables pour la mise en œuvre de l’agent IA.
- Être à l’aise avec la manipulation du Machine Learning, des datasets …
- Maîtriser le langage Python

Research area: Wassim Bouachir is a professor of computer science at TÉLUQ (University of Québec). His research work focuses on the development of novel machine learning and computer vision methods for a wide range of applications, such as security, environment sciences, and health-care systems.

Student roles:
The intern's tasks in the speech emotion recognition project include: 1. conducting a literature review, 2. investigating and incorporating new learnable feature extraction filters into the CNN-n-GRU model, 3. training and evaluating the model on publicly available datasets as well as the provided dataset, and 4. documenting the findings and methodologies. These tasks aim to enhance the model's performance and contribute to the advancement of speech emotion recognition.

Skills required:
To work on a speech emotion recognition project, the intern should have technical skills in machine learning, deep learning frameworks like TensorFlow or PyTorch, neural networks, and programming languages like Python. He/she should have experience in data analysis, feature engineering, and speech-specific signal processing techniques. Strong research, problem-solving, and communication skills are necessary, along with a passion for machine learning and AI.

Research area: My research focuses on optical neural networks and quantum photonic circuits through the co-design of electronic and photonic components. I develop integrated photonic systems that address challenges such as bandwidth mismatches between electronic and photonic circuits, high power consumption, and RF losses. To demonstrate and validate these concepts, I have designed and developed a range of integrated photonic devices, including modulators, photodetectors, electro-optic switches, and optical filters on silicon-on-insulator (SOI), silicon nitride, and indium phosphide (InP) platforms. My research interests include quantum computing, silicon photonics, quantum photonic circuits, optical neural networks, Electronic–Photonic Co-Design and FPGA-based hardware acceleration.

Student roles:
1. Perform optical mode simulation using Lumerical Mode, Interconnect and qInterconnect software
2. Implement a software version of the Quantum Circuit using IBM Qiskit/Xanadu Pennylane
3. Writing a summary report,
4. Designing Quantum Photonic Circuit

Skills required:
1. Python programming language (or coding experience on any C-like language)
2. Basic linear algebra, matrix and statistics
3. Lab experiment on 1st and 2nd year computer engineering and electrical engineering subjects.

Research area: Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants.

Student roles:
Design, implement and validate the behavior of the algorithm.

Skills required:
System Architecture, Robotic Operating System, Gazebo, C, C++, Python, JuliaRobotic, RoboDK, Vibe Coding

Research area: I'm an Associate 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 imbalance problem;
- Implement in Python and experimental setting to train and test the use of standard feature selection methods, and novel feature selection methods developed for high dimensional imbalanced problems. The experimental workflow should allow the comparison of the results of: i) using the standard feature selection methods; and ii) using feature selection methods developed for high dimensional imbalanced problems.
- Run the experiments in Python and make the necessary adaptations to the developed feature selection methods.
- Write a report that describes the algorithms developed, 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.
To work in this project the student should have:
- Basic knowledge and experience with machine learning algorithms
- Experience setting up and running experiments in Python
- Good written and communication skills.

Research area: Machine Learning

Student roles:
- Design a Few-Shot Learning framework tailored to anomaly detection in industrial time series data, capable of generalizing unseen fault types using minimal labeled samples across various fault types and operating conditions.
- Explore and compare multiple FSL algorithms, including metric-based, Memory-Based, and transformer-based models.
- Examine the impact of time series encoding on FSL model performance and discrimination capabilities.
- Validate the potential of FSL by the proposed approaches on real industrial datasets with varying types of sensors.

Skills required:
- Machine learning: data processing, feature extraction, classification, and evaluation metrics.
- Signal processing: Time series encoding and anomaly detection techniques
- Python programming: with libraries like PyTorch, TensorFlow, and scikit-learn.

Research area: Our program studies how digital health tools are designed, implemented, and sustained in real care settings, with a focus on chronic disease self-management and patient-reported outcomes. Recent work examines how language models and retrieval-augmented systems can support patients and clinicians without displacing clinical judgment. We combine implementation science, mixed methods, and co-design with patients and providers, and we run our own data and modelling infrastructure. Chronic pain self-management is a current priority: we are building AI-supported coaching that stays close to the behavioural evidence and to what patients and clinicians find usable.

Student roles:
The two interns will work on linked parts of the same pipeline, with enough overlap to support each other.
One intern will focus on the evidence side: searching and screening the behavioural literature on chronic pain self-efficacy, extracting the interventions and their active components into a consistent format, and organizing them into a structured knowledge base the coaching system can query. This intern will read a large volume of research literature, do structured data extraction, and document decisions so the corpus is reproducible.
The second intern will focus on the system side: connecting the knowledge base to a retrieval-augmented language model, writing the code that retrieves relevant evidence for a given coaching situation, and shaping prompts so the generated content keeps a clear link back to the source. This intern will do most of the programming and help test how retrieval and generation work together.
Both interns will take part in evaluating the pipeline. They will check the system's coaching output against the source evidence for fidelity, and bring outputs to team meetings where clinical and patient-partner input judges safety and usefulness. Both will meet regularly with the supervisor and the team, write up what they build, and present their work at the end of the internship.
We expect the interns to contribute to design decisions, not only execute tasks. Across twelve weeks, each will produce a documented, working component — an evidence corpus and knowledge base, or a retrieval-and-generation prototype with an evaluation — that the lab will keep using. Prior experience with every part is not expected; we provide the clinical and methodological context and pair each intern with team members already on the project.

Skills required:
We are looking for two students from computer science, software or data engineering, cognitive or health sciences, or a related field. You should be comfortable reading research articles and pulling structured information out of them, and have some programming experience (Python preferred). Any of the following helps but none is required: language models or retrieval/RAG systems, behavioural or qualitative science, health research, and data structuring or knowledge bases. We especially welcome students who are careful with detail, document their work clearly, and are curious about how evidence becomes a usable tool. Working language is English; French is welcome.

Research area: I work in interdisciplinary optical sciences. My research spans the gamut from physics to biomedical engineering to vision science. My work is primarily theoretical/computational. However, much of my work has a strong biomedical bias.

Student roles:
Coding and simulations of optical systems using genetic algorithms. preparing papers and abstracts. Will interact with other grad students in discussions, problem solving, etc. as well as assistance in computer coding.

Skills required:
The student should be a self-starter and independent. Good programming skills are required (C++, Matlab,Mathematica, etc.). Knowledge of optical engineering is desirable but not required. Quantitative skills necessary. Knowledge of/familiarity with optical design software such as Zemx or Code V is a plus but not necessary. Knowledge of genetic algorithms.

Research area: LAR.i aims to design, create and implement intelligent and sustainable logistics included in a digital corridor. Instrumentation of the logistics chain (via a digital corridor) will make it possible to locate cetaceans and make their positions available via real-time visualization on a maritime map dedicated to pilots and ports. An optimal trajectory to follow, considering a set of constraints, will be proposed by this new type of navigation decision support system. It will thus be possible to direct ships to less risky sectors.

Student roles:
Design, implement and validate the behavior of the algorithm.

Skills required:
Robotic Operating System, Gazebo, C, C++, Python, JuliaRobotic, RoboDK, Vibe Coding, architecture système

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

Student roles:
The student will contribute to the design and development of the proposed Graph-RAG framework for extreme wind disaster management. Their tasks will include reviewing the relevant literature on retrieval-augmented generation, knowledge graphs, and disaster-response decision support; collecting and organizing relevant datasets and documents; helping construct the graph-based knowledge representation; implementing components of the retrieval and reasoning pipeline; and assisting in testing the framework on case studies involving hurricanes and tornadoes. The student will also participate in the analysis and interpretation of results, preparation of visualizations, and documentation of the research outcomes. Depending on progress, the student may also contribute to the preparation of a conference paper, journal manuscript, or technical report.

Skills required:
The student should have a background in one or more of the following areas: computer science, data science, artificial intelligence, civil engineering, environmental engineering, or a related discipline. The student should have programming experience, preferably in Python, and a basic understanding of machine learning or natural language processing. Familiarity with large language models, retrieval-augmented generation, knowledge graphs, graph neural networks, or database systems would be an asset. The student should also be comfortable reading scientific literature, handling heterogeneous datasets, and working in an interdisciplinary research environment.

Research area: Professeure en informatique de la santé à la faculté de médecine et des sciences de la santé et à la faculté des sciences de l’Université de Sherbrooke. Je m’intéresse à la modélisation de bases de données temporelles, les langages de définition et d’interrogation de bases de données à l’aide d’ontologies, l’analyse et le raisonnement temporels, la génération de graphes de connaissances et l’intégration de données pour les systèmes de santé apprenants.

Student roles:
Le projet UHF-SQL consiste à :
• élaborer les règles d’équivalences entre les composants du modèle temporel et les composants SQL pour plusieurs SGBDR ;
• définir des gabarits de génération de code SQL performant;
• définir les critères de comparaison de performance de transactions et de requêtes ;
• définir des fonctions et des requêtes pour construire des trajectoires ;
• définir les scénarios d’essais (jeux de données, les requêtes et les transactions) ;
• évaluer le composant avec des scénarios de test.

Skills required:
Les connaissances/compétences suivantes sont un atout :
* Programmation : SQL, Java, StringTemplate, JooQ, Gradle
* Outils : PostgreSQL, MSSQL, DB2, DataGrip, Git

Research area: I am the founder and director of the ECCoLe Lab (Edge Computing, Communication, and Learning) at INRS-EMT. Our research focuses on efficient AI computing and wireless communication systems, combining expertise in computer architecture, embedded systems, machine learning, and wireless signal processing. Our main activities include: (1) energy-efficient hardware accelerators for deep learning; (2) lightweight AI techniques for wireless communications; and (3) optimization of deep learning models for real-time edge deployment. ECCoLe promotes a highly interdisciplinary environment bridging AI algorithms with practical hardware and communication system implementations. To learn more about our projects and research activities, please visit our website: https://inrs-eccole.github.io/index.htm

Student roles:
The intern will contribute to a research project focused on hardware-efficient transformer inference for edge AI systems. The main responsibility will be to investigate and evaluate approximate implementations of the softmax function used in transformer attention mechanisms. The intern will perform literature review, implement and test approximation techniques in Python using frameworks such as PyTorch, and analyze their impact on model accuracy and computational complexity.

The student will also participate in FPGA-oriented performance evaluation by estimating hardware resource utilization, latency, and energy efficiency of different approximation methods. Depending on progress and background, the intern may contribute to the implementation of selected designs using FPGA development tools.

The project will involve experimental evaluation, data analysis, documentation of results, and participation in regular research meetings. The intern will work closely with graduate students and the research supervisor, gaining practical experience in AI hardware acceleration, efficient deep learning systems, and edge computing research.

Skills required:
The candidate should have a background in electrical engineering, computer engineering, computer science, or a related field, with interest in AI and hardware acceleration. Basic knowledge of machine learning and neural networks is expected, along with programming experience in Python and familiarity with frameworks such as PyTorch or TensorFlow. Understanding of digital hardware concepts and FPGA systems is an asset, but prior FPGA design experience is not mandatory. Strong analytical skills, motivation for research, and the ability to work independently are highly desirable.

Research area: Our research lab studies physical human-robot interaction in the context of collaborative industrial robotics. Specifically, we specialize in the following fields: 1) Robotic manipulator dynamics modelling, which is important for identifying the full capabilities of a manipulator’s actuators. 2) High-performance motion optimization, which is vital for exploiting these capabilities. 3) High-performance computing for robotics control and optimization, which provides manipulators with the ability to react quickly to a dynamic environment. 4) Human-robot interaction modelling, and detection allow humans and robots to work together to achieve a task that neither would be able to do as efficiently alone.

Student roles:
The student will work in coordination with the lab director and a graduate student to:
- Train on the use of Nvidia GPU (CUDA) programming tools.
- Transcribe robotic trajectory computation algorithms into highly parallelizable algorithms for exploitation of high-performance GPU computing.
- Transcribe algorithms for computing the inverse dynamics of robot manipulators that are parallelizable for GPU computing.
- Write tests to validate the accuracy of GPU-based calculations.
- Write tests to compare the computation time between the initial algorithms and the new algorithms.
- Write a report on the project and indicate potential avenues to increase the success rate of future work.

The following objectives will be considered extras to be completed if the above objectives are quickly met:
- Develop parallelizable nonlinear optimization algorithms for GPU.
- Assist other interns and graduate students with their experimental work.

Skills required:
Candidates must be students in the final stages of an undergraduate program in mechanical, electrical, mechatronics, software, computer engineering or an equivalent program. The successful candidate should have skills and interest in high performance C or C++ programming. For this project, the successful candidate must be familiar with the basic operation of computer memory and the ability to understand highly technical computer documentation. Knowledge of numerical optimization techniques will be considered an asset.

Research area: Cybertsécurité, IoT, Gestion d'incidents, analyse de risque, conception d'architectures sécurisées

Student roles:
-Sélectionner des datasets de référence publics représentatifs de données sensibles.
-Implanter des mécanismes de confidentialité différentielle (DP-SGD, Laplace, PATE) dans une chaîne d’entraînement en Python avec PyTorch ou TensorFlow.
-Entraîner des modèles avec et sans confidentialité différentielle sur ces datasets.
-Simuler des attaques d’inférence d’appartenance pour quantifier les fuites de données résiduelles.
-Comparer les approches selon la performance du modèle (précision, F1-score), les garanties de confidentialité (ε, δ), et le coût computationnel.
-Optimiser les hyperparamètres (bruit, clipping, budget ε) pour trouver le meilleur compromis entre protection et utilité.
-Produire un code propre et documenté, un rapport final d’analyse comparative, et une grille de recommandation méthodologique.

Skills required:
-Bonnes connaissances en apprentissage automatique (entraînement de modèles, évaluation, jeux de données).
-Notions de base en confidentialité différentielle (théorie ou pratique : DP-SGD, mécanisme de Laplace, etc.).
-Capacité à implémenter et à adapter des algorithmes de protection de la vie privée.
-Rigueur scientifique et esprit d’analyse.
-Autonomie et capacité à documenter son travail (code, rapports).
-Bonne communication en français

Research area: I'm an Associate 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 imbalanced regression in data streams;
- implement in Python different pre-processing methods for dealing with the imbalance regression problem in data streams. These methods will need to automatically decide if over-sampling or under-sampling is needed for different regions of the target variable. Then, they will apply those methods to obtain a new training data that will be used to generate a model and obtain the predictions for the next case(s).
- Implement an evaluation framework to compared the results of i) not applying any transformation to the data streams; and ii) applying the developed pre-processing methods
- Write a report that describes the experiences carried out and 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.
To work in this project the student should have:
- Basic knowledge and experience with machine learning algorithms (such as random Forest, Neural Networks, SVM, etc)
- Experience setting up and running experiments in Python
- Good written and communication skills.

Research area: My research focuses on optical neural networks and quantum photonic circuits through the co-design of electronic and photonic components. I develop integrated photonic systems that address challenges such as bandwidth mismatches between electronic and photonic circuits, high power consumption, and RF losses. To demonstrate and validate these concepts, I have designed and developed a range of integrated photonic devices, including modulators, photodetectors, electro-optic switches, and optical filters on silicon-on-insulator (SOI), silicon nitride, and indium phosphide (InP) platforms. My research interests include quantum computing, silicon photonics, quantum photonic circuits, optical neural networks, Electronic–Photonic Co-Design and FPGA-based hardware acceleration.

Student roles:
1. Perform optical mode simulation using Lumerical Mode, Interconnect and qInterconnect software
2. Implement a software version of the Quantum Circuit using IBM Qiskit/Xanadu Pennylane
3. Writing a summary report,
4. Designing Quantum Photonic Circuit

Skills required:
1. Python programming language (or coding experience on any C-like language)
2. Basic linear algebra, matrix and statistics
3. Lab experiment on 1st and 2nd year computer engineering and electrical engineering subjects.

Research area: The Principal Applicant is actively engaged in the Scholarship of Teaching and Learning (SoTL) in human anatomy and physiology within nursing education. His research focuses on evaluating the effectiveness of innovative teaching technologies, including the three-dimensional (3D) virtual human cadaver (Anatomage). He examines how well nursing students retain and apply foundational knowledge in subsequent years of study. Based on identified gaps, he develops targeted interventions using Anatomage, immersive learning technologies (AR/VR), and artificial intelligence. These approaches aim to bridge early- and later-year learning, ultimately enhancing academic performance and supporting long-term knowledge retention among nursing students.

Student roles:
The selected student will develop machine learning models to enable the automated labeling of medical images using established frameworks such as MedSAM, nnU-Net, and AI. The student will train these models on a newly developed dataset and evaluate their performance by analyzing and summarizing improvements in labeling accuracy.

Skills required:
This project is an interdisciplinary collaboration between a faculty member from the Department of Computer Science at MacEwan University and the Principal Investigator (Dr. Narnaware) from the Department of Nursing Foundation and Science, Faculty of Nursing, who will contribute domain expertise in labeling medical images using MedSAM, nnU-Net, and AI. We seek a student with a background in computer science, complemented by strong English communication skills, to effectively support the project activities. Potential Mitacs students' skills should include the following: Programming and courses in AI (which could include Computer Science or engineering).

Research area: Our research focuses on the neuronal substrates involved in the control of human locomotion. Areas of expertise include neuroscience, motor control, computer science and engineering. We use techniques such as movement kinematics, EMG, neuronavigated brain stimulation (TMS) and brain imaging with MRI and PET. We study human locomotion both overground and on a split-belt treadmill. We acquire data in young and older adults, trained athletes as well as in people with Parkinson's disease.

Student roles:
Analyze raw data from the inertial sensors in Matlab or similar software. Provide user-friendly instructions or interface for use by non-experts. Collaborate with graduate students to conduct the project.

Skills required:
Interest in human motor control. Interest in teamwork.

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:
• Conduct a literature review on document structure parsing, multimodal layout-aware models, and NLP methods applied to financial disclosures and fund reports.
• Collect and preprocess a large set of annual mutual fund reports, linking each report to its fund-level metadata (e.g., CIK, series, class).
• Experiment with multimodal LLMs for robust section detection in noisy or unstructured reports.
• Develop alignment strategies to associate extracted content with the correct fund entities in multi-fund disclosures.
• Evaluate extraction accuracy through human-labeled validation sets and error analysis.
• Build prompting and fine-tuning pipelines to explore the predictive value of narrative sections (e.g., market outlook, risk tone).
• Contribute to writing reports and publications documenting results, models, and challenges.
• Present findings in lab meetings or workshops to encourage feedback and improve methods collaboratively.

Skills required:
Expertise in any of these fields: Computer Science, Computer Engineering, Information Science and Technologies. Additional Finance knowledge or expertise is a PLUS. Prior programming experience in Python. Experience with Large Language Models prompting and Fine-tunning. Experience with PyTorch. 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: Mon domaine de recherche est centré sur l’intelligence ambiante, la vision par ordinateur, les capteurs connectés et les systèmes intelligents pour la reconnaissance d’activités humaines, l’assistance personnalisée et l’analyse comportementale. J’utilise des techniques avancées d’intelligence artificielle, incluant les modèles fondamentaux, l’apprentissage fédéré et la distillation de modèles, pour développer des systèmes fiables, respectueux de la vie privée et efficaces en temps réel dans des contextes domestiques, médicaux ou sociaux.

Student roles:
Durant la phase initiale à distance, l’étudiant réalisera une revue de la littérature sur les méthodes de reconnaissance d’activités et les architectures multimodales. Il ou elle participera à la collecte, au prétraitement et à l’annotation de données issues de capteurs variés (caméra, IMU, détecteurs). Un premier prototype sera conçu pour classifier les activités dans des scénarios simples.

L’étudiant développera et testera différentes architectures de réseaux neuronaux profonds pour combiner les modalités et reconnaître les séquences d’activités en contexte domestique. Il ou elle explorera l’utilisation de modèles fondamentaux et d’algorithmes de compression (distillation) pour déployer des modèles légers, ou d’apprentissage fédéré pour simuler des déploiements multi-utilisateurs respectueux de la vie privée.

Lors du séjour au Canada, l’étudiant poursuivra les tests en environnement semi-réel ou simulé (ex. : maquette d’appartement connecté ou données de partenaires). Il ou elle développera un prototype fonctionnel intégrant capteurs et algorithmes, avec interface de visualisation des activités ou alertes.

Le stage comprendra également des réunions régulières de suivi, des échanges scientifiques et la participation à la rédaction d’un rapport ou brouillon d’article scientifique résumant la méthodologie, les résultats expérimentaux et les perspectives de déploiement.

Skills required:
Le candidat devra avoir de solides bases en apprentissage profond, traitement de séquences, et reconnaissance d’activités. Une expérience en traitement de données multimodales (images, IMU, sons) est un atout. Des compétences en Python, PyTorch, TensorFlow, OpenCV ou MediaPipe sont souhaitées. L’autonomie, la curiosité scientifique et l’intérêt pour les applications sociales ou médicales sont fortement valorisés.

Research area: In the context of the project, I provide my related specialized research experience. My pioneering work in healthcare technologies has advanced the development of wearable sensors and intelligent healthcare systems. I created a simple capacitive electrocardiogram (ECG) sensor embedded in a cotton T-shirt, enabling comfortable, long-term heart monitoring through a lightweight, low-power device that produces clinically reliable signals. Building on this innovation, I developed sensor-based algorithms capable of analyzing walking patterns to identify age-related mobility differences and detect potential health risks, including musculoskeletal, cardiovascular, and fall-related issues. He also pioneered a prototype smart medical home that integrates sensors, autonomous software,

Student roles:
The student researcher will contribute to the design, development, testing, and evaluation of wearable healthcare sensors and smart medical home technologies. Responsibilities may include collecting and analyzing physiological and mobility data, implementing signal processing and machine learning algorithms, conducting experiments, and validating system performance. The student will assist in integrating sensor hardware with software platforms, documenting research findings, and participating in team meetings and technical discussions. They will collaborate with faculty members and fellow researchers to address real-world healthcare challenges, support technology development, and contribute to publications, presentations, and knowledge dissemination activities.

Skills required:
The ideal intern or student researcher should have a strong background in engineering or a related discipline. Knowledge of sensors, embedded systems, signal processing, data analytics, machine learning, or software development would be highly beneficial. Experience with programming languages such as Python or MATLAB, as well as familiarity with data collection and analysis, is desirable. The student should possess strong problem-solving, communication, and teamwork skills, and an interest in healthcare technologies, wearable devices, smart homes, and digital health. Curiosity, initiative, and the ability to work in interdisciplinary research environments are essential.

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

Student roles:
This project is ideal for undergraduate computer science students interested in:
• Deep learning and foundation models
• Signal processing and wireless communications
• Real-world AI systems with impact on future networks
Students will gain hands-on experience working with large-scale data, modern neural architectures, and real-world AI challenges, making this project a strong foundation for research careers, graduate studies, or advanced industry roles in AI and wireless systems.

Skills required:
Basic Python programming Interest in machine learning or data analysis
Familiarity with signals, wireless systems, or time-series data (helpful but not required)
Willingness to learn deep learning tools (e.g., PyTorch)
Comfort with math fundamentals (linear algebra, probability)
Ability to work independently and communicate clearly

Research area: My research focuses on the interaction between large-scale computing infrastructure and electric power systems. I study how data centers can be safely and efficiently connected to power grids while managing their rapidly growing electricity demand. Key research topics I am conducting include grid interconnection studies, power-flow analysis, reliability assessment, demand flexibility, and coordinated planning of computing and energy infrastructure. The broader goal is to develop practical methods that help utilities, system planners, and data-center operators accommodate new AI workloads without compromising grid reliability.

Student roles:
The student will work closely with a diverse research team to develop and evaluate models for data-center interconnection studies. The project will begin with a review of how large electricity consumers are connected to transmission and distribution systems, with particular attention to the challenges created by fast-growing data center loads.

The student will then build a simulation environment using standard power-system test networks and open-source analysis tools. Initial tasks may include modeling data centers as large loads, running numerical and power flow simulations, identifying congested lines or voltage-limit violations, and studying how the impacts vary across candidate interconnection locations. The student may also analyze contingency scenarios, load-growth conditions, and different assumptions about data-center demand profiles.

Based on these findings, the student will help investigate improved interconnection strategies by designing algorithms. Potential directions include selecting suitable connection points, estimating the need for network upgrades, coordinating data-center demand with grid conditions, and evaluating flexible load mechanisms such as workload shifting or temporary demand reduction. The student may also explore optimization-based approaches for siting and capacity planning.

The student will participate in regular research meetings, maintain clear documentation of the models and code, summarize experimental results, and prepare a final presentation and technical report. Depending on the results, the student may contribute to an open-source research tool or a manuscript for publication.

Skills required:
The student should have a background in electrical engineering, energy systems, computer engineering, operations research, or a related field. Proficiency in Python, MATLAB, or Julia is required. Familiarity with mathematical modeling power and energy systems, optimization, numerical methods, or data analysis will be helpful. Prior experience with power flow analysis, renewable integration, or energy-system modeling is preferred but not required. The student should be comfortable learning new software tools, reading technical papers, conducting simulation studies, and interpreting quantitative results.

Research area: I am interested in the application of operation research techniques (mathematical modeling, decision under uncertainty, combinatorial optimization) and artificial intelligence (deep learning, reinforcement learning, graph neural networks) to different fields such as industrial scheduling, time scheduling, transportation and logistics, and healthcare.

Student roles:
The student will conduct a literature review on recent approaches in artificial intelligence, optimization, and decision support. She will then contribute to the development of innovative methods that integrate artificial intelligence and mathematical programming to support decision-making in the fields of Industry 5.0 and supply chain management. Their tasks will include data analysis and preprocessing, the design and implementation of predictive and prescriptive models, the execution of computational experiments, the evaluation of the performance of the proposed approaches, and the dissemination of research findings.

Skills required:
Knowledge of either machine learning/artificial intelligence or optimization/mathematical programming is required. Candidates with an interest in developing expertise in the complementary area are encouraged to apply.
A very good knowledge of the English or French language

Research area: Dr. Ajay K. Dalai is a distinguished professor of Chemical and Biological Engineering. He is the former Canada Research Chair of Bioenergy and Environmentally friendly chemical processing (2001-2024). He has been leading a research group of about 30-40 graduate students and post-doctoral fellows since 2000 in the Catalysis and Chemical Reaction Engineering Laboratories, at the University of Saskatchewan. Dr. Dalai's research focuses on finding cleaner and more sustainable ways to produce energy and useful products from waste materials. His work includes converting agricultural waste, plastics, and low-value materials into fuels and other valuable products while reducing environmental pollution.

Student roles:
The student will work closely with post-doctoral researchers involved in biomass conversion and machine learning applications. Responsibilities will include collecting and organizing experimental and literature data, performing data cleaning and preprocessing, developing and validating machine learning models, and conducting statistical analyses. The student will gain hands-on experience using Python-based machine learning libraries and data visualization tools to analyze HTL process performance.
In addition, the student will assist in interpreting model results, identifying key process parameters affecting bio-oil production, and evaluating model performance using appropriate statistical metrics. The intern will also contribute to technical reports, research presentations, and scientific publications. At the end of the internship, the student will present the findings to the research group and prepare a comprehensive report summarizing the project outcomes.

Skills required:
The ideal candidate is an undergraduate student in Chemical Engineering, Environmental Engineering, Computer Science, Data Science, Mechanical Engineering, Agricultural Engineering, or a related discipline. Basic knowledge of statistics, thermodynamics, biomass conversion technologies, and data analysis is desirable. Familiarity with programming languages such as Python or R and experience with spreadsheet-based data handling will be considered an asset. Strong analytical, problem-solving, and communication skills are essential.

Research area: Dr. Afshin Rahimi’s specialized research area focuses on aerospace systems engineering, with particular emphasis on unmanned aerial vehicles (UAVs), autonomous systems, spacecraft attitude dynamics and control, and intelligent health monitoring of aerospace components. His research integrates advanced modeling, control theory, machine learning, and data-driven techniques for applications in aerospace systems, including fault diagnosis, structural and thermal analysis, and real-time system optimization. He is also actively involved in multidisciplinary engineering education and the development of computational tools for aerospace research and simulation.

Student roles:
1. Conduct a comprehensive literature review on reaction wheel fault mechanisms, fault diagnosis techniques, and machine learning methods relevant to aerospace systems.

2. Perform simulations using Bialke’s highly nonlinear reaction wheel model under nominal and faulty operating conditions.

3. Generate, organize, and preprocess simulation datasets corresponding to healthy conditions and various reaction wheel fault scenarios.

4. Apply and evaluate different machine learning algorithms for fault detection, classification, and root-cause identification.

5. Analyze model performance using statistical metrics and visualization techniques to assess the effectiveness and robustness of the developed approaches.

6. Present research progress and findings during regular meetings with the research team and contribute to technical documentation and reporting.

Skills required:
MATLAB/Simulink
Python
Engineering Modeling
Machine Learning

Research area: My research focuses on advanced polymer composites and additive manufacturing for aerospace applications. I specialize in 3D printing of thermoplastic and thermoset composites, with emphasis on carbon fiber reinforced materials, non-planar toolpath optimization, and mechanical performance characterization. Current projects include self-healing polymers, recycled plastic optimization using machine learning, and impact resistance testing of composite structures. My lab combines experimental materials science with computational modeling to develop next-generation manufacturing processes for lightweight, high-performance aerospace components.

Student roles:
The student will actively participate in all phases of this gradient printing research project over the 12-week internship:

**Weeks 1-3: Training & FullControlXYZ Development**
- Learn multi-material 3D printing fundamentals and dual-extrusion mechanics
- Install and explore FullControlXYZ framework (Python-based G-code generation)
- Develop gradient generation algorithms: linear, exponential, sine-wave transitions
- Create test specimens with simple gradients (rigid-to-flexible PLA-TPU)
- Calibrate dual-extrusion printer for smooth material transitions

**Weeks 4-7: Gradient Fabrication & Optimization**
- Print 20-30 gradient specimens with varying transition profiles (length, rate, pattern)
- Optimize FullControlXYZ parameters: extruder switching frequency, purge volumes, temperature zoning
- Characterize print quality: measure delamination, surface finish, dimensional accuracy
- Document process parameters and failure modes (clogging, oozing, poor adhesion)
- Iterate on gradient algorithms to minimize defects

**Weeks 8-10: Materials Characterization**
- Conduct mechanical testing along gradient axis: 3-point flexural tests at 5-10 positions per specimen
- Measure property gradients: stiffness, strength, elongation, or conductivity (depending on materials)
- Map property transitions: correlate FullControlXYZ mixing ratios with measured properties
- Compare continuous gradients vs. discrete material blocks (baseline comparison)
- Analyze data: identify optimal transition profiles for smooth property changes

**Weeks 11-12: Reporting & Knowledge Transfer**
- Compile gradient-property relationships into design charts/graphs
- Create final report with gradient printing guidelines and material recommendations
- Prepare presentation for lab group and potential conference submission
- Document FullControlXYZ scripts and share as open-source contribution
- Train future students on gradient printing protocols

**Mentorship & Development:**
- Weekly 1-on-1 meetings with Prof. Tabiai for guidance and feedback
- Access to graduate students (PhD/Master's) for peer learning
- Training on dual-extrusion 3D printers and mechanical testing equipment
- Opportunity to co-author conference paper if results are significant
- Contribution to FullControlXYZ open-source community

**Deliverables:**
- Validated gradient printing protocol using FullControlXYZ
- Quantified property-gradient

Skills required:
Strong background in mechanical engineering, materials science, or manufacturing engineering. Coursework in materials characterization, polymer science, or additive manufacturing preferred. Basic understanding of 3D printing processes (FDM/FFF) and dual-extrusion systems. Programming experience with Python (intermediate level - variables, loops, functions, libraries). Interest in functional materials and multi-material systems. Familiarity with data analysis tools (Excel, Python pandas/matplotlib). Hands-on experimental mindset with attention to detail. No prior FullControlXYZ experience required - full training provided. English proficiency required; French is an asset but not mandatory.

Research area: Deep learning, multimodal neural networks (text + image), few-shot learning, vision-language models, drone remote sensing, endangered plant species

Student roles:
The student will:
1. Conduct a literature review on multimodal few-shot learning and vision-language models applied to fine-grained plant recognition.
2. Curate a dataset pairing our drone imagery of endangered Canadian plant species with botanical text descriptions from eFloras and related sources.
3. Design and implement a multimodal architecture that aligns plant image embeddings with botanical text embeddings — exploring CLIP-based approaches and custom fusion networks, guided by experiments and the literature.
4. Benchmark the model in few-shot settings against image-only baselines, measuring the gain brought by textual descriptions.
5. Write a final report and, if results are promising, co-author a conference paper.

Skills required:
Required:
- Solid experience with deep learning frameworks (PyTorch)
- Knowledge of computer vision and image classification
- Familiarity with natural language processing and text embeddings
- Ability to read and critically review scientific literature

Strong assets:
- Prior work with vision-language models (CLIP, BLIP-2, or similar)
- Experience with few-shot learning
- Familiarity with remote sensing or aerial/drone imagery

Research area: mobile computing, data analytics, wireless networks

Student roles:
The main responsibilities of student include: data collection, data processing, algorithm validation and implementation optimization of sleep staging, bed exit detection and fall detection.

Skills required:
- Knowledge in signal processing and experiences in python and Matlab are required.
- Prior experience in machine learning preferred

Research area: Mes recherches se situent à l’intersection de l’intelligence artificielle, de la vision par ordinateur, de la simulation numérique et de l’optimisation des processus industriels. J’explore l’intégration de capteurs, l’analyse d’images et de signaux, ainsi que l’utilisation de modèles d’apprentissage profond (incluant les modèles fondamentaux, l’apprentissage fédéré et la distillation) pour améliorer la performance, détecter les défauts, et automatiser les processus de production. Mon expertise s’applique aux environnements industriels intelligents et à l’industrie 4.0.

Student roles:
Pendant la phase à distance, l’étudiant effectuera une revue des méthodes existantes en détection de défauts industriels et en simulation intelligente. Il ou elle travaillera à la construction ou à l’exploitation de bases de données d’images ou de signaux industriels, développera des algorithmes de traitement d’image et entraînera un premier modèle de détection.

Une attention particulière sera portée à la conception de pipelines de traitement efficaces, à la sélection de bons indicateurs de performance, et à l’intégration de techniques récentes comme la distillation pour optimiser les modèles.

Au cours de la phase en présentiel au Canada, l’étudiant pourra simuler un environnement industriel ou utiliser des jeux de données capturés par des partenaires industriels. Il ou elle intégrera l’IA dans un flux de production virtuel, testera des scénarios d’optimisation, et évaluera les bénéfices opérationnels. L’étudiant pourra également concevoir un module d’interprétabilité des résultats (ex. : heatmaps, attention maps) pour faciliter la prise de décision.

Il ou elle devra documenter le système, participer aux réunions hebdomadaires et contribuer à la rédaction d’un rapport de recherche ou d’un article scientifique. Le projet vise une application concrète et transférable à des contextes industriels réels.

Skills required:
L’étudiant doit maîtriser Python et avoir des compétences en vision par ordinateur, apprentissage profond, et simulation numérique. Une expérience avec des environnements comme PyBullet, Unity ou ROS est un atout. La connaissance des bibliothèques comme OpenCV, PyTorch, NumPy, ou TensorFlow est souhaitée. Un esprit analytique, une capacité à résoudre des problèmes complexes et à s’adapter à des cas d’usage réels sont essentiels.

Research area: My expertise is in machine-learning based algorithm design for future 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 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 work in collaboration with a research team. Key project tasks will include:
• In-depth literature review: The student is expected to become familiar with the key concepts of federated learning, deep reinforcement learning, and wireless networks.
• Design and implementation of algorithms: The student will have an active role in the design of algorithms adapted to the specificities of wireless networks and asynchronous federated learning. This can include modeling problems, selecting states and actions, as well as setting up the necessary neural networks.
• Experiments and evaluation: The student will work on the implementation of the proposed algorithms and evaluate them using realistic scenarios or appropriate datasets. The objective is to demonstrate the performance improvement of asynchronous federated learning using DRL in wireless networks.

This project offers a unique opportunity to explore the emerging areas of asynchronous federated learning and deep reinforcement learning in wireless networks. It will allow the student to develop his/her skills in research, algorithm design and programming, while contributing to the advancement of knowledge in the field.

Skills required:
Wireless networking and wireless communications. Evaluation of algorithms complexity. Machine learning. Deep reinforcement learning. Wireless network simulations (ex., Python).

Research area: The Smart Sustainable Systems and Automation Lab (S³A Lab) is a research facility within the Division of Engineering at Saint Mary’s University. The lab is dedicated to advancing mechatronics, autonomous control, and sustainable energy technologies. By integrating Artificial Intelligence (AI) and the Internet of Things (IoT) with traditional engineering disciplines, the S³A Lab develops innovative solutions for the complex challenges of modern energy management and industrial automation.

Student roles:
The student will implement PV equivalent circuit models, perform parameter estimation using optimization techniques, analyze I–V data, compare model accuracy, and support the validation and presentation of results using programming language.

Skills required:
Required Skills/Background of the Student
- Undergraduate student in Engineering, Computer Science, Applied Mathematics, or a related field.
- Basic programming skills in MATLAB, R or Python.
- Familiarity with data analysis, numerical methods, and visualization.
- Understanding of basic electrical circuits and/or renewable energy systems is an asset but not required.
- Interest in optimization methods and computational modeling.
- Strong analytical thinking, problem-solving skills, and attention to detail.

Research area: Chemometrics and analytical chemistry FTIR, Raman and 1H-NMR spectroscopy Multivariate data analysis Matlab and python developments Food sciences

Student roles:
Participate in team developments with the third-year student
Respond to the requests of the professor responsible for the internship
Regular reporting of actions carried out in terms of completed tasks and tests performed
Have ethics in programming and respect the rules regarding the sharing of source codes developed within the team

Skills required:
good command of programming in languages used for full-stack development (Front-end/back-end)
basic knowledge of the Matlab environment (knowledge of scripting logic in Matlab)

Research area: Power engineering, condition monitoring, signal processing, artificial intelligence

Student roles:
• Establish criteria or indicators for fault detection through the transfer function and its estimation.
• Propose and develop algorithms for interpretation and identification, allowing to precisely determining the nature and extent of deformations.

Skills required:
electrical engineering, dielectric and electric tests, signal processing, machine learning, programing

Research area: My research focuses on hydrology and statistical hydroclimatology, with an emphasis on predicting water extremes such as floods and droughts. I develop advanced predictive modelling approaches and decision-support frameworks to enhance water security and resilience in both natural and built environments. My research group, the Hydroclimate Extremes and Water Security Lab (HEWSL), addresses complex challenges across pluvial, fluvial, and coastal systems to improve how communities are planned, designed, and adapted to thrive amid water extremes in a changing Earth system.

Student roles:
The student will work with the research team to lead the implementation, testing, and optimization of state-of-the-art prediction methods for water extremes. They will be responsible for translating these methods into efficient, well-structured R code and contributing to the development of both the R package and an associated user interface (e.g., graphical user interface or web application).
The role includes ensuring that the code is accurate, reliable, and well-documented, following best practices in reproducible research and software development. The student will also participate in validating model performance, troubleshooting computational challenges, and refining the tool to enhance usability and functionality for end users.

Skills required:
Required Qualifications/Skills:
- Background in computer science, water resources, statistics, or a related field, with a strong interest in hydrology and data-driven modelling.
- Programming skills are essential.
- Ability to work independently while collaborating effectively within a research team.

Preferred Skills (not required, but beneficial):
- Programming experience in MATLAB (basic) and R (intermediate to advanced).
- Familiarity with statistical methods, particularly frequency analysis or bias correction/downscaling.
- Strong problem-solving skills, attention to detail, and an interest in developing reproducible, well-documented code.

Research area: - RISC-V - FPGA - Microelectronics - Embedded systems - Sensors - AI

Student roles:
- Carry out a review of the literature on RISC-V, FPGA and AI

- Design a digital system using VHDL, Verilog or SystemVerilog.

- Simulate the system using the tools provided for this purpose.

- Test the system on FPGA.

- This project could possibly constitute the start of a master’s degree.

Skills required:
- Knowledge of FPGA
- Basic knowledge of electronics would be an asset
- Basic knowledge of AI would be an asset

Research area: The Advanced Real-Time Simulation Laboratory (ARS) is an advanced Modeling & Simulation research laboratory, located in the Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada. The Laboratory is physically located at the Carleton University Centre for Visualization and Simulation (V-Sim). The laboratory is investigating means of automatic generation of executable models derived from systems specifications. The research is based on the Discrete EVent System specification (DEVS) formalism, and willaugment previous work with new theory, methodology, and supporting development tools. We are also interested in integrating the simulation results obtained with

Student roles:
The candidate will follow the methodology for developing real-time embedded application. A target application will be identified (to be discussed with the candidate according to his/her background and interests), and a complete application will be developed from scratch using our techniques and tools (which include advanced visualization tools, a development environment, and specialized hardware). The activities will be carried out in the CFI Advanced Laboratory for Real-Time Simulation (ARS). This infrastructure consists of a high-performance computing platform (64 high speed processors linked with a very high speed interconnect) to support an advanced real-time simulation engine (including AD/ DA interfaces and graphics workstations for human interaction).
Expected learning opportunities include:
• an introduction to modeling and simulation tools
• fine tuning C++ skills
• fine tuning Java skills
• real-time and embedded systems development techniques
• experience in an advanced high performance computing environment

Skills required:
• C++ programming
• Java programming
• With preference, some experience with FPGA hardware

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 self-learning strategies;
- Understand the main resampling methods to deal with imbalanced domains;
- Implement in Python an experimental setting to train and test the integration of different pre-processing methods in semi-supervised learning problems that suffer from the class imbalance problem;
- Run the experiments in Python and make adaptations to the pre-processing methods or implement new ones that may be more suitable to improve the performance of the self-learning methods under imbalanced domains;
- Write a report that describes the experiences carried out and 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: Abdullah's research focuses on the development of robotic systems, with a particular emphasis on three core areas: therapeutic robots for the rehabilitation of individuals with impaired upper limbs, assistive robotic devices designed to enhance daily living, and exoskeleton robotic systems aimed at augmenting human mobility and strength. In addition to these focal areas, he has made significant contributions to the advancement of mechatronic system design. His expertise and leadership have been instrumental in securing and managing numerous research projects supported by both industry and funding organizations. His work has resulted in over 90 publications in international journals and conferences.

Student roles:
• Literature Review: Analyze existing feeding devices and identify gaps in handling cultural and food diversity.
• Define Design Requirements: Set clear functional, cultural, and technical requirements for the system
• System Design and Architecture: The student will collaborate with research team members to design the software architecture for integrating the robotic feeding device with vision sensors. This process involves defining modules for image acquisition, object detection, utensil alignment, and user control interfaces. The team will also establish data flow, communication protocols, and real-time processing requirements.
• Control and Interface Development: The student will develop software that converts vision data into actionable commands for the robotic feeding system. A user interface will be implemented to enable users to control or guide the feeding process. The design will prioritize responsiveness, safety constraints, and usability.
• Iterative Refinement: Refine design and controls based on feedback and testing outcomes.
• Documentation and Reporting: Document all design stages, findings, and results for publication and review.

Skills required:
Applicants should be senior undergraduate or master's students specializing in computer engineering, systems engineering, electrical engineering, or computer science. Candidates are expected to demonstrate proficiency in at least four of the following areas: microcontroller programming, sensor interfacing, data acquisition, robotics and mechatronics, software engineering, artificial intelligence, human-computer interaction, computer vision, machine learning, Python, Robot Operating System (ROS) software development, and data analysis.

Research area: A specialist in intelligent manufacturing, Dr. Sattarpanah Karganroudi conducts research based on strategies specific to Industry 4.0, particularly non-destructive evaluation and 3D metrology of mechanical components and structures. Also involved in R&D, he has used experimental approaches to develop engineering, precision, and materials processing methods and laser welding. Computer-aided design, manufacturing and inspection, optimization processes, finite element analysis, augmented reality, and digital simulation are frequently used in his work.

Student roles:
The student will actively contribute to various stages of the project, including design, development, experimentation, and analysis. Their responsibilities will encompass both theoretical and practical aspects, requiring a multidisciplinary skill set and a proactive approach.
The student will conduct an extensive literature review to gain a comprehensive understanding of existing research and technologies related to robotized 3D geometric inspection. They will identify relevant methodologies, algorithms, and tools utilized in the field and synthesize this knowledge to inform the project's direction.
The student will play a key role in the design and development of the robotized inspection system. They will work closely with the research team to select a suitable robotic manipulator and customize it for the inspection tasks. Proficiency in programming languages such as Python or C++ will be essential for developing computer vision algorithms for 3D reconstruction, feature extraction, and system control. The student will collaborate with experts in robotics and computer vision to integrate these components into a cohesive system.
The student will actively participate in setting up the experimental environment, selecting appropriate test components, and establishing calibration procedures. They will be responsible for data collection, ensuring that it is conducted accurately and consistently. The student will also assist in maintaining the experimental equipment and troubleshooting any issues that arise during the data collection process.
Upon collecting the data, the student will be involved in analyzing the results and evaluating the performance of the developed system. They will work closely with the research team to quantify metrics such as measurement errors, repeatability, and inspection time. The student will contribute to comparing the outcomes with traditional manual inspection methods, highlighting the advantages and improvements achieved by the robotized system.

Skills required:
The student undertaking this research project on robotized 3D geometric inspection would benefit from a strong foundation in robotics, computer vision, and metrology. Proficiency in programming languages such as Python or C++ is crucial for developing and implementing computer vision algorithms. A solid understanding of robotics principles and experience with robotic manipulators is essential for integrating the inspection system. Familiarity with metrology techniques and calibration procedures is necessary to ensure accurate measurements. Additionally, knowledge of industrial quality control processes and a proactive approach to problem-solving would be advantageous for the successful completion of this project.

Research area: IoT security

Student roles:
- Develop a Few-Shot Learning (FSL)-based framework for attack detection that can generalize across new, unseen IoT devices with minimal labeled examples.
- Incorporate Federated Learning (FL) to maintain data privacy and leverage distributed learning across multiple devices.
- Design robust ensemble strategies to enhance noise tolerance and improve overall detection performance.
- Evaluate performance on benchmark IoT datasets with varying levels of noise and device diversity.

Skills required:
- Machine learning: data processing, feature extraction, classification, evaluation metrics.
- IoT security: knowledge of attack types and IoT protocols.
- Python programming: with libraries like PyTorch, TensorFlow, scikit-learn.

Research area: My work is at the intersection of haptic technologies and human-computer interaction (HCI). I explore how the user experience (UX) of human-computer interfaces can be improved by introducing rich tactile feedback. I develop and experiment with advanced haptic interfaces that stimulate the sense of touch by vibrations, ultrasounds, textures, or other means. I prototype interfaces that use the sense of touch and evaluate them experimentally with users. I’m interested in a wide range of applications including wearable computing, augmented reality, virtual reality, and the Internet of Things (IoT).

Student roles:
You will lead the project, with supervision from prof. Levesque and help from other HUX lab members. You role will be to (1) familiarize yourself with prior work on metamaterials and vibration transmission and (2) experiment with different 3d-printed structures to understand how the transmission of vibrations can be controlled. Optionally, you may also (3) simulate vibration propagation in finite-element modelling software, (4) write software to automatically create propagation paths in 3D printed objects, or (5) develop application concepts for vibration routing in 3D printed objects.

Skills required:
The ideal candidate would have experience in 3D printing and mechanical engineering. However, any student with relevant skills to contribute in some way to this project will be considered. If in doubt, please apply and we will see if the project can be adapted to your particular skills and interests.

Research area: Mon domaine de recherche concerne l’acquisition, le traitement et l’utilisation des données géospatiales, en particulier en arpentage et en génie géomatique. Mes projets sont décomposés en trois axes, étant le premier l’acquisition et la préparation des données acquises, le deuxième la formalisation des connaissances permettant l’automatisation de l’extraction des informations et le troisième une analyse et la modélisation de la qualité des données acquises. Ce projet s’insère dans les deux derniers axes étant donné que l’information géospatiale doit être traitée, analysée et modélisée avec l’information juridique en utilisant l’IA.

Student roles:
L’étudiant jouera un rôle très important dans le développement d’une méthode permettant l’intersection entre la donnée géospatiale et l’information juridique avec l’IA. Il participera dans un projet d’ampleur et sera en étroite collaboration avec un étudiant au doctorat ainsi que d’autres stagiaires. Il participera, plus spécifiquement, à l’extraction de l’information juridique et son géoréférencement. Pour ce faire, il devra d’abord effectuer une revue de littérature afin de sélectionner des méthodes pertinentes pour l’extraction de l’information et son association à de l’information spatiale. Il sera responsable de la mise en œuvre de scripts simples pour l’extraction d’entités spatiales et de relations géographiques à partir des textes. Il devra également contribuer à la structuration des données extraites et à leur conversion en formats compatibles avec les systèmes d’information géographique (SIG).
En parallèle, l’étudiant devra documenter rigoureusement ses démarches, analyser les résultats obtenus et produire des rapports clairs et synthétiques. Ces documents seront destinés à être publiés en libre accès, ce qui exige une qualité rédactionnelle assez élevée. Il devra aussi participer aux réunions d’équipe, partager ses avancées, proposer des pistes d’amélioration et s’adapter aux retours de l’équipe de recherche sur ses travaux. Au sein de l’équipe, l’autonomie, la créativité et la capacité à travailler en collaboration sont très valorisées dans un environnement multidisciplinaire à l’interface du droit, de la géomatique et de l’intelligence artificielle.

Skills required:
Il doit posséder une base solide en statistiques et en programmation (traitement du langage naturel). Il doit également posséder des bonnes compétences rédactionnelles pour formaliser les analyses et produire des documents clairs, destinés à être diffusés en libre accès. Une connaissance de base dans l’information juridique et foncière est fortement souhaitée, afin de comprendre l’importance de l’information présente sur les documents fonciers. Une sensibilité aux enjeux juridiques et à la structure des textes législatifs est un atout. L’étudiant doit faire preuve d’autonomie, de rigueur et de créativité, tout en étant capable de collaborer efficacement au sein d’une équipe multidisciplinaire.

Research area: My research is focused on safe autonomy, intelligent transportation systems, mobile robotics, AI, robot perception and control for autonomous systems. At the LACITS Lab we design and implement AI for decision making and control for autonomous vehicles and robots, focusing on safety, human to robot interaction, uncertainty, real time and physical feasibility. Our current projects include: path planning for quadruped robots, vision-language-action models, safe reinforcement learning, trajectory generation, human-robot interaction, among others. We test our methods on physical robotics, on simulations, and using hardware-in-the-loop setups. Our overall goal is to develop trust-worthy autonomous systems that can safely interact with people.

Student roles:
You will support the development and implementation of a safe AI-enabled navigation for a quadruped robot. One student will go through the whole pipeline, while two students will work on two separate but complementary parts of the pipeline: (1) perception and vision-language-action, (2) safety filtering / trajectory evaluation / robot command feasibility.

First, the selected papers and software related to the perception of a robot, the vision-language-action model, safe navigation and the quadruped robot itself will be reviewed by the student(s). Then, the software modules will be implemented and tested by the student(s) in Python, MATLAB, etc. The tasks that the student(s) will perform are for example: processing the visual data, detecting a human or obstacle, generating simple navigation commands, checking safety constraints, running simulations, plotting the results of the performance of the robot, documenting the implemented methods.

Meetings with all team members will be held on a regular basis to present progress, to discuss troubles and to ensure that all team members are working according to the initial plan. All team members are responsible for organizing their code and for writing adequate documentation for the work they have done. The student(s) will also be able to compare the safety-aware framework that is being developed with a number of simpler approaches and measure their performance using a number of metrics including task completion, collision avoidance, smoothness of motion, command feasibility, and execution time.

There is potential for the intern(s) to support a controlled laboratory test of the safety-aware framework on a physical implementation using a Unitree Go2 quadruped robot. The intern(s) would also support the development of a short technical report, a video of the demonstration, and/or a conference-style manuscript.

Skills required:
This project is intended for a student with some programming experience in Python, MATLAB, or similar language and an interest in one of the following topics: robotics, AI, computer vision, control or autonomous systems. Experience with deep learning, RL, ROS, robot simulation, Git, or mobile robots is welcome but is not required. A highly motivated undergraduate student, who is willing to learn, read research papers, write code, analyze data, document results and work independently, is the ideal candidate. Strong curiosity, reliability, problem-solving skills, attention to detail and good communication skills are more important than prior experience with the topics above.

Research area: My research work focuses on improving the security of computer systems, especially at the digital hardware design level. My work involves RTL/microarchitecture design, with RTL/gate-level simulation, as well as FPGA-based prototyping. I am also interested in exploring the implications of emerging machine learning techniques on the IC supply chain and system-on-chip life cycle. My research interests include embedded systems design and electronic design automation. I am also interested in understanding security-related topics in machine learning (both security of ML and applications of ML to security).

Student roles:
The student will build (or contribute to building) the experimental framework and run structured three-way comparisons (purely algorithmic, LLM-only, and hybrid) across each targeted design phase. The other task might include investigating how factors such as prompt strategy affect the results. Moreover, documentation of the process is part of the role.

Skills required:
The student should be comfortable with HDL implementation and with designing and executing controlled experiments (e.g., defining appropriate evaluation metrics to assess the LLM effectiveness). Some familiarity with LLM APIs or prompt engineering is a plus.

Research area: The research of our group is at the intersection between physics, chemistry and biology. We utilize optical methods to study biophysics problems, including protein dynamics, as well as energy and charge transfer processes, for instance those occurring in the context of photosynthesis. We also build our own instruments (or modify existing ones), write our own software and engage in computer modeling.

Student roles:
The student will have to familiarize himself/herself with the existing code and acquire some understanding of underlying physics background in order to be able to introduce meaningful changes to the code. Supervisor will help student in identifying which parts of the code require modifications and which should be left alone. Once changes are introduced, the student will have to test the software and determine if it produces physically reasonable results. The last stage of the project will involve modeling and comparisons with the available experimental results. The student will have to discuss his/her progress and results with the supervisor and occasionally produce written reports and figures. The project may result in a publication.
Participation in the experiments generating the data to be compared to simulation results is possible and if student so desires the project can be tilted in this direction as well.

Skills required:
The ideal candidate is a software engineering or computer science student with interest in physics and scientific programming in general, or a physics or mathematics student with interest in programming and decent programming skills. Specifically, the existing software/code is written in C++ under MS VisualStudio. However, strong logical thinking capabilities, understanding of algorithms and ability to write code accordingly are much more important than experiences with a particular programming language.

Research area: Mes travaux de recherche portent sur l’intelligence artificielle appliquée au monitoring des états cognitifs et physiologiques de l’humain. Cela inclut l’analyse de signaux physiologiques (EEG, ECG, EDA, etc.), de données multimodales (vidéo, audio, mouvements) ainsi que le développement de modèles d’apprentissage profond, de modèles fondamentaux et de techniques comme l’apprentissage fédéré. L’objectif est de créer des systèmes robustes pour la prévention des risques, en particulier en milieux sensibles tels que la route ou les environnements industriels.

Student roles:
L’étudiant débutera son stage à distance. Dans un premier temps, il ou elle devra effectuer une revue de littérature sur les indicateurs de vigilance et les approches d’IA pertinentes. Ensuite, il ou elle explorera les jeux de données disponibles (publics ou simulés), effectuera des prétraitements (filtrage, normalisation, segmentation), et développera un premier prototype de modèle d’estimation basé sur une ou plusieurs modalités (signaux physiologiques, images, etc.).

Pendant la phase en présentiel au Canada, l’étudiant approfondira le développement du système. Il pourra intégrer de nouvelles sources de données (capteurs inertiels, vidéos, etc.), tester des approches d’apprentissage fédéré pour une meilleure protection des données, et appliquer des techniques de distillation pour alléger les modèles. Il ou elle contribuera aussi à la conception d’un pipeline d’analyse complet (acquisition – prédiction – interprétation), qui pourrait servir dans un contexte réel ou semi-simulé (simulateur de conduite ou poste de travail simulé).

Le projet se conclura par une analyse comparative des approches testées, la rédaction d’un rapport de recherche ou d’un brouillon d’article scientifique, et la documentation du système. L’étudiant participera à des rencontres hebdomadaires pour discuter de ses progrès et obtenir un encadrement méthodologique.

Skills required:
L’étudiant doit être à l’aise avec Python et les bibliothèques d’IA comme PyTorch ou TensorFlow. Il devra avoir des connaissances de base en traitement de signaux et en apprentissage automatique. Une expérience avec les données physiologiques ou biométriques (ECG, EEG, EDA) est un plus, de même que des notions en vision par ordinateur ou en fusion multimodale. Une bonne autonomie, une curiosité scientifique, et de bonnes capacités d’analyse sont requises.

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

Student roles:
The intern will build upon an existing functional prototype that currently utilizes on-screen visual feedback. The first major milestone involves hardware and software integration: the intern will connect and program a physical RGB-enabled mechanical keyboard, developing the logic to shift visual scaffolding from the screen directly to the physical keys. Once the multisensory feedback system is operational, the intern will transition to user research. They will assist in conducting empirical data collection sessions with children, directly observing and quantifying the system's impact on user experience (UX), engagement, and typing performance metrics.

Skills required:
We are seeking a highly motivated intern with a background in Computer Science, Software Engineering, or Human-Computer Interaction (HCI). Required skills include proficiency in programming (C/C++ or Python) and an understanding of state-machine logic. Familiarity with hardware prototyping (e.g., microcontrollers, ESP32, Raspberry Pi) and handling basic I/O components like screens and RGB keyboards is highly desirable. The ideal candidate possesses a strong interest in educational technology and tangible interfaces. They should be eager to learn how to bridge software logic with physical hardware to create impactful, user-centered experiences for children.

Research area: My research work focuses on improving the security of computer systems, especially at the digital hardware design level. My work involves RTL/microarchitecture design, with RTL/gate-level simulation, as well as FPGA-based prototyping. I am also interested in exploring the implications of emerging machine learning techniques on the IC supply chain and system-on-chip life cycle. My research interests include embedded systems design and electronic design automation. I am also interested in understanding security-related topics in machine learning (both security of ML and applications of ML to security).

Student roles:
Students in this project will work to develop their understanding of hardware security issues and then work towards the design and prototyping of the hardware security test-bed. They will survey the literature for hardware security flaws, implement hardware blocks in Verilog/SystemVerilog/VHDL, and integrate the designs with a processor to produce a system-on-chip. They will be expected to participate in regular lab group meetings and work in close collaboration with graduate students and the academic supervisor.

Skills required:
Students interested in this project should be comfortable with hardware design languages (Verilog/SystemVerilog is preferred, although VHDL is good too), general scripting, and FPGA development. Cybersecurity experience is not required, but having an interest is a must. Familiarity with version control and assembly/C-programming is a bonus.

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

Student roles:
Implementing new recurrent architectures, analyze their learning, design new algorithms for training RNNs.

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

Research area: My research aspires to develop robot capabilities that show potential to provide people with improved workplace safety, greater job satisfaction, deeper personal development, and overall better quality of life. For example, in health care, robots could care for people who require quarantine conditions. Healthcare professionals would be safer from risky exposure or repetitive work that might injure them, freeing up their time to focus on the quality of their interactions and patient assessments. In such scenario, Dynamic Motion Intelligence, the capacity to exploit robot dynamics to adapt in a rapidly changing scenario while seamlessly interacting with humans, is crucial.

Student roles:
The students role is to contribute to the lab by testing new and novel ideas along with adapting known implementations to the lab infrastructure.
He is to be and active member of the group discussions and be unafraid to ask questions and try things.
It also means learning fast, reviewing scientific literature and improve coding skills to convert theory to implementation.

Skills required:
Student should have experience coding as well as the ability to learn a new programming language.
Ideally with engineering background
Student should have a well developed problem solving skills.
A curiosity driven mindset.
Be unafraid to try, fail and learn.
Code is mainly in Python so experience with python is a must, but C++ and Julia are a good to have and a plus.
ROS 2 experience is encouraged, but not required.
Passionate about robotics is very useful.

Research area: I am interested in the application of operation research techniques (mathematical modeling, decision under uncertainty, combinatorial optimization) and artificial intelligence (deep learning, reinforcement learning, graph neural networks) to different fields such as industrial scheduling, time scheduling, transportation and logistics, and healthcare.

Student roles:
- understand the problem
- review recent literature on recommendation systems for the management of type 1 diabetes
- design recommendation algorithms for the data sets at hand
- test and compare the performance of these algorithms
- participate in the writing of a scientific article

Skills required:
Very good knowledge of deep learning and reinforcement learning
Excellent knowledge of the Python language
Very good ability to work in a team
A very good knowledge of the English or French language

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 implement an algorithm developed by the supervising professors and collaborators. Depending on the student's skillset and interest, the focus can lie either on pure implementation or also in fine-tuning the algorithm, in particular in developing the machine learning aspects.

The internship will take place at UQAM, at the Centre de recherche mathématiques (CRM), the Centre de recherche sur l’intelligence en gestion de systèmes complexes (CRI2GS) and the Interuniversity centre of network logistics (CIRRELT). The student will be able to participate in activities (seminars etc.) at all of these places.

Skills required:
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).

Research area: Vision-language models (VLMs), vision-language-action (VLA) pre-training, self-supervised annotation pipelines, and autonomous navigation on ground robots

Student roles:
The student will:
• Reproduce the Poutine VLT pre-training pipeline on the CoVLA dataset to build a working baseline and understand the chain-of-thought annotation format;
• Design and implement an off-road annotation prompt for an open-source VLM (Qwen2.5-VL 7B or equivalent), adapting Poutine’s structured labelling schema to off-road-specific object classes and terrain conditions;
• Generate VLT annotations on an off-road driving dataset (RUGD, RELLIS, TartanDrive, or lab rosbag recordings) and fine-tune a compact 3B VLM using the VLT next-token prediction objective;
• Evaluate trajectory prediction quality offline (ADE, FDE) and conduct an ablation study comparing language-conditioned vs. language-free trajectory prediction;
• Package the trained model as a documented ROS inference node to facilitate future online deployment on a Jackal or Husky UGV;
• Write a technical report and, if results are strong, co-author a research paper or workshop submission.

Skills required:
Required:
- Strong Python skills and experience with PyTorch and Hugging Face Transformers.
- Familiarity with large language models or vision-language models (fine-tuning, prompting, inference)
- Experience with robotics data (rosbags, point clouds, camera images) or autonomous driving datasets is a strong plus.
- Ability to read and critically review scientific literature

Strong assets:
- ROS2

Research area: My broad research area focuses on the design of X-ray and nuclear medical imaging devices. Within this multidisciplinary context, my specific interests lie in the design of distributed systems for data acquisition and the integration of edge computing. The technologies used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), embedded field-programmable gate arrays (eFPGAs), and networking.

Student roles:
Working alongside current students, the MITACS intern will be responsible for coding verification modules in Python, as well as modifying and adding original HDL digital circuits designed to extract experimental data. Data analysis will also utilize Python modules for visual data representation (histograms, linear regression, etc.). The internship will conclude with an oral presentation to the research group.

Skills required:
Requirements: Basic training in HDL programming (Verilog/VHDL), object-oriented programming with Python, Python debugging skills, experience using the Linux command line, and familiarity with basic laboratory equipment (oscilloscope, power supply).

Research area: My lab develops AI methods for measuring human cognitive and physiological state from synchronized EEG, eye tracking, gait, facial video, and wearable sensors. We focus on signal processing, multimodal sensor fusion, biomedical AI, and interpretable machine learning for attention, fatigue, workload, and performance monitoring in realistic environments.

Student roles:
Two interns will work as a coordinated acquisition-and-analysis pair. Intern 1 will help prepare protocols, run participant sessions, calibrate EEG/eye-tracking/wearable devices, monitor signal quality, and maintain structured metadata. Intern 2 will focus on preprocessing and modeling: artifact cleaning, synchronization checks, feature extraction, model training, and performance visualization. Both interns will participate in weekly lab meetings, literature review, ethics-compliant data handling, and preparation of reproducible code. By the end of the placement, the team should deliver a pilot dataset, documented acquisition workflow, baseline fatigue/classification models, and a short technical report suitable for conversion into a conference poster or undergraduate research presentation.

Skills required:
Students should have programming experience in Python and an interest in machine learning, signal processing, neuroscience, physiology, or wearable sensing. Useful background includes NumPy/pandas, scikit-learn or PyTorch, basic statistics, EEG/biomedical signals, or experimental data collection. Reliability, careful documentation, and comfort working with human-participant data are important.

Research area: Digital signal processing, audio signal processing, software defined radio,

Student roles:
This project is to work on the “Cocktail Party Problem” which is a “Blind Speech Separation Problem”. There are two methods to tackle this problem- “Time domain methods” and “Frequency domain methods”. One approach is “Frequency domain Blind Speech Separation using Independent Component Analysis”. ICA introduces permutation misalignments in frequency domain. There have been many methods to solve the misalignments. But these methods fail in a more reverberant environment The first step it to developing a channel model for the Impulse Response to solve the permutation problem in a more reverberant environment. Existing models do a fair job of separation for Narrow-band separated microphones. The job is to propose a model of IR including early reflections and a reverberant tail, and test the applicability of it with microphones separated by more than one wavelength, and develop an algorithm to separate the sources (speakers). The research group has a large database of measured data using up to 8 sources and 48 microphones. This database is available for testing algorithms.

Skills required:
Digital Signal Processing
MATLAB

Research area: This research focuses on learning-based visual servoing and control of continuum robotic manipulators mounted on moving robotic platforms. The project combines robotics, computer vision, machine learning, and control systems to enable flexible manipulators to autonomously interact with dynamic environments using onboard visual feedback. By integrating machine learning, model predictive control, and physics-based simulation, the research aims to improve motion planning, target tracking, and manipulation capabilities for aerial and mobile robotic systems operating in uncertain and unstructured environments.

Student roles:
The student will assist in developing the simulation environment, implementing visual servoing and learning-based control algorithms, and conducting experiments to evaluate system performance. The student will analyze results, prepare technical documentation, and participate in regular research discussions with the supervisory team. Contributions toward publications and open-source software development are also expected.

Skills required:
The ideal student should have a background in robotics, mechatronics, computer engineering, or control systems. Experience with Python or C++ programming is required. Familiarity with ROS, computer vision, machine learning, or reinforcement learning frameworks is considered an asset. Knowledge of robotics simulation tools such as Gazebo, PyBullet, or SOFA is an asset. The student should be motivated to work on interdisciplinary problems involving robotics, AI, and autonomous systems.

Research area: Software defined radio and radar uses digital signal processing as close to the antenna as possible, thus mnimizing the need for analog radio frequency circuits, and enables the same hardware to perform many different radio functions such as broadcast reception, cellphone base station, data telemetry, two way public safety radio. The research is to develop new radio algorithms, implement and test.

Student roles:
Literature review
Project planning
Programming
Testing
Documentatation

Skills required:
Digital Signal Processing
MATLAB
wireless communications