Engineering

Research area: Tissue engineering applied to the peripheral nervous system aims to develop innovative strategies to promote the repair and regeneration of damaged nerves. This field is notably based on the development of biomaterials capable of replicating the physiological environment with a particular focus on personalized approaches. The use of stem cells, particularly to generate support cells such as Schwann cells, represents a key focus. Moreover, the development of clinically relevant human in vitro culture models constitutes a major challenge for improving our understanding of regenerative mechanisms and accelerating the translation of innovations into clinical practice.

Student roles:
The student will actively participate in research activities related to the project, under the technical supervision of a doctoral student. The student will be trained in the various experimental methods required to carry out the project, enabling he/her to acquire practical laboratory skills and become familiar with approaches used in tissue engineering.
Initially, the student will be expected to gain a solid understanding of the project’s scientific and methodological objectives, particularly through the reading and analysis of relevant scientific articles and discussion with the professor and doctoral student. In the laboratory, the student will assist the doctoral student with experiments and daily tasks. These activities will include, among others, cell culture, scaffold synthesis and characterization, protein and nucleic acid extraction, as well as the use of molecular biology techniques such as qPCR and immunostaining to monitor gene and protein expression, along with epifluorescence microscopy analyses.
The student will also contribute to data collection, analysis, and interpretation. The student will be encouraged to develop autonomy and critical thinking skills, and to take part in scientific discussions within the team. She will regularly present the progress of her work during the research group’s weekly meetings.
Finally, the internship will conclude with the writing and presentation of a detailed technical report summarizing the project’s objectives, methods, results, and main conclusions.

Skills required:
The candidate should be familiar with tissue engineering and possess a basic background in cellular and molecular biology in order to understand and manipulate the biological systems involved in nerve regeneration. Knowledge of biomaterials (synthesis, characterization, and handling) is also required for the design and characterization of suitable structures. Practical experience in cell culture, as well as familiarity with perfusion culture systems, will be considered strong assets. In addition, skills in scientific programming (e.g., Python, R) and 3D (bio)printing will be regarded as advantages for contributing to the development of innovative, multidisciplinary models.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 17 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with material characterization
training will be provided.

Skills required:
Mechanical/production engineering
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: The HeART (Healthcare Applications for Robotic Technologies) Lab is focused on the fundamental understanding and development of magnetically actuated small-scale robots for surgical and onchip applications. This involves investigating the design, fabrication, and control of these robotic devices. HeART Lab director Dr. Onaizah has been involved in the development of the first shapeforming magnetic continuum robots to navigate along tortuous anatomical paths and experimentally demonstrated this in realistic in vitro environments. The HeART works closely with clinicians to solve some of the most challenging problems at the forefront of medical robotics.

Student roles:
As a regular part of the team, the intern will be given a desk in one of the team offices. They will be integrated into a group of around 10 students working directly on medical robotics, under the direct supervision of Dr. Onaizah. In addition to this group, the intern will be a regular member of the HeART Lab, and benefit from its associated research ecosystem (e.g., seminars, interactions with other students, facilities). The intern will progressively take ownership of their project using a rampup approach. The first couple of weeks of the internship will be dedicated to understanding the research task ecosystem, and the different paths they can take in this context. Paired to a senior member of the team (PhD or Masters' student), careful analysis, reviews and guidance will be provided during the ramp-up period, so that eventually the intern can become more independent and can take initiative on their project. When reaching this point, an iterative and incremental approach will be used to tackle the research objective of this internship: a research question will be designed with their immediate supervisor, and the associated research will be done to tackle this question (including literature review, design analysis, experiments, coding, empirical analysis, when
relevant to the research question). Finally, the obtained results will be presented to the other members during a group meeting, and the next steps and research directions will be discussed collectively.

Skills required:
Minimum Requirements:
• Background in one of the following disciplines: Mechanical Engineering, Biomedical Engineering,
Electrical Engineering, Robotics, Computer Science, or related disciplines
• A willingness to work effectively with staff, students, and visitors from a wide range of
backgrounds.
Experience in one of the following areas would be an asset:
• Robot Operating System (ROS) and its Gazebo simulation package
• Object-oriented programming language (e.g., embedded C, Matlab, Python, C++, Java, or
LabVIEW)
• Computer Aided Design software (e.g. Solidworks, AutoCAD)
• Computer Aided Manufacturing (3D printing, laser cutting, CNC milling)
• Magnetic Control or Manipulation
• Microfabrication

Research area: The medical imaging physics lab here at Carleton specializes in: 1. x-ray imaging 2. Positron emission tomography 3. Geometry calibration x-ray CT 4. 3D real-time tracking 5. Image-guided radiation therapy and interventions.

Student roles:
The student is to improve the 3D-printed pneumatic motors used by the dynamic phantom and perform experiments to evaluate the system. More specifically:
1. Improve the 3D printed parts to reduce the leaking of compressed air.
2. Improve the mechanical system to reduce the vibration of the motion.
3. Optimize the system performance in terms of speed and repeatability.

Skills required:
The student needs to have:
1. Good hands-on skills with tools and prototyping.
2. 3D modeling and designing skills using CAD software.
3. 3D printing experiences.
4. Good programming capabilities (such as Python, C++, Java, or equivalent) .
5. Basic knowledge about electronics.

Research area: Dr. Saman Hassanzadeh Amin is a distinguished scholar with extensive contributions and expertise in operations research and supply chain management. His publications include over 69 papers in highly reputable peer-reviewed journals, and his work has attracted more than 6,400 citations on Google Scholar. His research activities have received external funding from several Canadian organizations, including NSERC and SSHRC. Dr. Amin’s areas of expertise include Supply Chain Management, Operations Management, Logistics, Machine Learning, Decision Support Systems, Operations Research, Optimization, and Data Science. His work primarily focuses on developing advanced optimization approaches and models to optimize operations and systems.

Student roles:
The student will design and optimize a sustainable diesel supply chain network under the supervision of Dr. Amin. Dr. Amin and his research team will collaborate and guide the student throughout the project. The student will first review the related literature and prepare a network figure for the problem statement. The student will use critical thinking and problem-solving skills to identify sustainable approaches that address existing gaps in the field, such as uncertainty. Then, the student will develop a multi-objective optimization model to design and configure the supply chain network. Afterward, the student will apply a solution approach and use the GAMS software to solve the model and analyze the results. Finally, the student and Dr. Amin will write a journal paper.

Skills required:
This project is related to optimization and operations research in industrial engineering and management science fields. Therefore, it is preferable that the student has previously passed a course in operations research, optimization, supply chain management, or mathematical modeling. Familiarity with GAMS or other optimization software is an asset. Strong English language skills are required for writing the journal paper.

Research area: Thermodynamique, Thermodynamique appliquée, Efficacité énergétique industrielle, Technologies avancées de réfrigération et de chauffage (solaire, géothermie, rejets thermiques), Optimisation des cycles organiques de Rankine (Biomasse, solaire, géothermie, rejets thermiques), Éjecteurs, Tubes Vortex, Intégration énergétique des procédés, Simulations dynamiques (TRNSYS et EES). Pompes à chaleurs (Haute température).

Student roles:
Dans le cadre de ce projet de recherche à la frontière du génie thermique et de l'innovation durable, l'étudiant assumera un rôle pivot alliant rigueur analytique avec un esprit critique dans une base de thermodynamique maitrisée. Sa mission se déclinera en deux axes principaux :
1. Développement et Validation Numérique:
L'étudiant sera responsable de la conception d'un modèle mathématique robuste sous EES (Engineering Equation Solver) ou MATLAB. Il devra traduire les phénomènes physiques complexes — tels que le transfert de masse membranaire et la séparation d'enthalpie au sein du tube vortex — en équations différentielles. Ce travail inclut une analyse exergétique fine pour cartographier les irréversibilités du système et optimiser les points de consigne (pressions, débits, concentrations).
2. Analyse et Communication Scientifique:
Au-delà de la technique, l'étudiant devra interpréter les données pour quantifier l'impact réel du système sur la réduction des GES et la consommation énergétique. Il sera activement impliqué dans la rédaction de rapports techniques et d'articles scientifiques destinés à des revues internationales.
Ce rôle exige donc une grande autonomie, une capacité à résoudre des problèmes d'ingénierie multidisciplinaires (thermo-fluides et mécanique) et une volonté de contribuer concrètement à la décarbonation des technologies du bâtiment.

Skills required:
Pour ce projet d'ingénierie hybride, le candidat idéal (niveau cycle d'ingénieurs) doit posséder les compétences et antécédents suivants :
Formation académique : Solide bagage en génie mécanique, génie thermique ou génie des procédés, avec une excellente maîtrise de la thermodynamique appliquée (cycles de puissance et transferts de masse/chaleur et réfrigération). Facilité d'écrire des rapports scientifiques, ou publications scientifiques
Compétences techniques : Compétences confirmées en modélisation numérique et programmation (idéalement sous EES, MATLAB ou ANSYS Fluent) pour la simulation des fluides.
Qualités personnelles : Rigueur scientifique, autonomie et esprit d'innovation face aux défis de l'efficacité énergétique.

Research area: My research focuses on Artificial Intelligence (AI) and data-driven methods for safety-critical engineering systems. Areas of interest include dynamic system modeling, predictive analytics, explainable AI, process monitoring, fault diagnosis, predictive maintenance, and risk assessment for industrial and engineering applications. By combining engineering principles with AI and machine learning techniques, this research aims to improve system reliability, predict failures before they occur, and support safer and more efficient operational decision-making in complex engineering systems.

Student roles:
The student will investigate how Artificial Intelligence and data-driven modelling techniques can be used to understand the behaviour of engineering systems and predict potential failures before they occur.
The student will work with simulation data from representative engineering systems such as vehicle suspension systems, DC motor control systems, tank level and storage systems, or other dynamic engineering processes. Using these systems as case studies, the student will explore methods such as DMD (for linear systems), SINDy(Non-linear systems), and machine learning approaches.
The student will implement selected algorithms in Python, evaluate model performance, compare prediction accuracy, and investigate how different techniques identify early indicators of abnormal operation and system degradation. Using the results obtained from simulations and modelling studies, the student will analyze system behaviour, create visualizations to communicate findings, and assess the strengths and limitations of different approaches for forecasting and failure prediction.
Throughout the internship, the student will participate in weekly research meetings, receive guidance from the supervisor and graduate student mentors, and develop skills in AI, machine learning, engineering simulation, scientific computing, and research methodology. The student will prepare a final technical report and research poster summarizing project outcomes.

Expected Outcomes
• AI-based dynamic models of representative engineering systems.
• Comparison of dynamic modelling and prediction techniques.
• Analysis of early indicators of system degradation and failure.
• Data visualizations and interpretation of system behavior.
• Experience with Python-based AI, simulation, and data analysis tools.
• Final technical report and research poster.
• Exposure to ongoing research in AI-enabled monitoring, predictive maintenance, and intelligent engineering systems.
• Potential contribution to future conference or journal publications.

Skills required:
The ideal candidate has a background in Engineering, Computer Science, Applied Mathematics, Data Science, Physics, or a related discipline. An interest in Artificial Intelligence, machine learning, dynamic systems, engineering applications, or predictive analytics is desirable. Experience with Python programming, data analysis, simulation, machine learning, or control systems is beneficial but not required. Strong analytical thinking, problem-solving abilities, and a willingness to learn new computational methods are important.

Research area: My research focuses on analog and mixed-signal integrated circuit design, semiconductor devices, and artificial intelligence (AI)-assisted electronic design automation (EDA). The work combines circuit design, simulation, optimization, and machine learning techniques to accelerate the development of advanced microelectronic systems. Current research includes AI-driven analog circuit optimization, layout- and parasitic-aware design methodologies, high-speed data-converter and transceiver circuits, and semiconductor technologies for harsh-environment and aerospace applications. The overall goal is to improve the performance, reliability, and productivity of next-generation integrated circuit design.

Student roles:
The student will work under the supervision of graduate researchers and the principal investigator as part of a collaborative research team focused on AI-assisted semiconductor design.

Responsibilities may include:
- Running and organizing circuit simulation experiments for representative analog circuit examples.
- Assisting with the generation, processing, and management of datasets used for AI model development.
- Developing and testing software scripts for simulation automation and data analysis.
- Extracting and organizing circuit performance metrics from simulation results.
- Supporting the validation of AI model predictions against circuit simulation data.
- Preparing figures, tables, and technical documentation summarizing results.
- Participating in technical meetings and discussions with the research team.
- Contributing to project reports and research dissemination activities.

Through these activities, the student will gain practical experience in circuit simulation, programming, data analysis, machine learning workflows, and semiconductor design automation while contributing to research at the intersection of artificial intelligence and microelectronics.

Skills required:
The student should be enrolled in Electrical Engineering, Computer Engineering, Engineering Physics, or a related discipline and have prior hands-on experience in analog integrated circuit design. The candidate must possess knowledge of CMOS analog circuits, semiconductor devices, transistor-level analysis, and analog simulation methodologies. Experience using Cadence Virtuoso for schematic capture, DC/AC/transient simulations, and circuit verification is required. Proficiency in Python for data processing and automation is highly desirable. Familiarity with machine learning concepts is an asset. The student should demonstrate strong analytical and programming skills and an interest in AI-assisted analog integrated circuit design.

Research area: Prof. Amar Mohanty is an internationally recognized leader in sustainable materials, bioplastics, biocomposites, and circular economy innovations. His research focuses on developing high-performance, value-added materials from biomass residues, industrial coproducts, and post-consumer waste streams while promoting environmentally sustainable manufacturing practices. His work spans biocarbon-reinforced composites, waste plastic valorization, sustainable alternatives to single-use plastics, and advanced materials for additive manufacturing and automotive applications. More recently, his research integrates artificial intelligence (AI) and machine learning (ML) tools to accelerate material design, process optimization, performance prediction, and sustainable product development, supporting the transition toward next-generation circular and low-carbon material systems.

Student roles:
The project provides an opportunity to participate in cutting-edge research focused on footwear waste valorization, co-pyrolysis, AI/ML-driven process optimization, and sustainable polymer composites development. Students will gain hands-on experience using state-of-the-art facilities at the Bioproducts Discovery and Development Centre (BDDC), University of Guelph.
1) Apply scientific research principles while working in a collaborative research environment;
2) Follow laboratory safety protocols and good laboratory practices;
3) Prepare samples and conduct pyrolysis, materials processing, and characterization experiments;
4) Collect, analyze, and interpret experimental data;
5) Apply critical thinking and problem-solving skills to evaluate research outcomes;
6) Gain hands-on experience with thermochemical conversion, polymer composites, thermal analysis, mechanical testing, and materials characterization techniques;
7) Assist in the development and application of AI/ML tools for data analysis and process optimization;
8) Develop written and oral communication skills through reports, group meetings, presentations, and scientific publications.

Skills required:
Undergraduate students in engineering, materials Science, physics, chemistry, or other related fields of study with some lab experience through classes or the workplace.
• Highly motivated to learn to prepare and test samples and operate minor lab equipment independently.
• Courses taken in Materials Science are highly desirable.
• Knowledge of polymers, fibers, and composites is an advantage.
• Excellent computer skills (Microsoft Office: Word, Excel, PowerPoint, etc.).
• Excellent communication skills (oral and written communication).
• Detail-oriented data recording.
• Diligent in maintaining safe work protocols.
• Knowledge of AI/ML is an added advantage.

Research area: My research focuses on climate adaptation and resilience in the building sector, combining geospatial analysis, building science, vulnerability assessment, and applied artificial intelligence to characterize building stock, building retrofits and resilience measures at the community scale. My work bridges building physics and vulnerability research, with an emphasis on cold-climate, equity-centred applications in Quebec, producing tools that inform climate adaptation, retrofit prioritization, and resilience policy.

Student roles:
Intern 1: review artificial-intelligence methods for building characterization, recommend the most applicable approach, and validate it on a representative imagery sample.
Intern 2: compile demographic data, apply a social vulnerability index, and produce interactive geospatial visualizations.
Intern 3: compile available building vintage and typological data, apply a scoring method, and produce interactive geospatial visualizations.
Intern 4: compile a registry of urban waste-heat sources with thermal output estimated from benchmarks, develop and validate a matching procedure independently, and apply it to the highest-vulnerability locations at the integration stage, producing interactive geospatial visualizations.

Skills required:
Intern 1: background in computer science, geomatics, or civil engineering; working command of Python; familiarity with machine-learning and vision models; proficiency in the critical synthesis of technical literature.
Intern 2 : background in geography; proficiency with Python and with geospatial data visualization.
Intern 3 : background in civil engineering, architecture, or building science; proficiency with Python and with geospatial data visualization.
Intern 4 : background in energy or mechanical engineering, or urban planning; proficiency with Python and with geospatial data visualization.

Research area: I am a recognized expert in signal processing, machine learning, and Al, as applied to biomedical engineering. My lab conducts broad research across areas of human movement, health, and happiness, including topics like human-machine interaction (e.g. prostheses, robots, VR/AR, etc.), rehabilitation engineering (e.g. spinal cord injury, stroke, and Parkinson's disease), gait and mobility monitoring (for both health and security applications).

Student roles:
The successful candidate will join a multidisciplinary team, comprised mostly of engineers, computer scientists, and clinicians. They will work with other students, ranging from undergraduate level to PhD, to postdoctoral fellows and Professors. They will become an active and important member of the Institute, contributing to 1) their own line of research (to be identified collaboratively with Dr. Scheme on or in anticipation of arrival), 2) the progress of the research team working on machine learning and ambient sensor-based gait and mobility analysis, and 3) the overall atmosphere at the Institute of Biomedical Engineering (including research jams - like hackathons, but for research and team building, social outings, journals clubs, and lunch breaks). The student will be encouraged to pursue publication of strong work and results stemming from their own line of research and/or from collaborations with graduate students.

The student will be expected to arrive with an open and curious mindset. They will be asked to review the relevant literature, work with the team to identify a clear and actionable research gap to work on for the summer, manage their time, attend weekly lab meetings to review and present progress, collaborate with others, and esnure that the results of their work (including research findings, results, code, and documentation) are effectively translated back to the team before they leave.

Skills required:
The candidate should have demonstrated experience in machine learning and Al, preferably with a strong background in Python. A background in biomedical engineering and/or human data is an asset, but not mandatory. Similarly, experience in security and/or biometrics is a bonus. The student must be able to work as part of a team, but be able to work independently and manage their time between meetings.

Research area: My current research interests include: • Artificial intelligence (AI) for wireless networks • 5G and 6G enabling technologies • Reconfigurable intelligent surfaces and smart environments • Physical layer security in wireless networks • Optical wireless communications • Machine learning for wireless resource management and optimization • Hybrid optical wireless and radio frequency communications • Underwater communications

Student roles:
The student will work under my supervision in collaboration with Ph.D students and Master students, and will participate in algorithm development and simulation.

Skills required:
Required background and skills:
Wireless communications systems
Machine learning
Good programming skills

Research area: At TRIUMF - Canada's National Laboratory for Nuclear and Particle Physics we operate a unique Laser Ion Source to ionize short lived, radioactive isotopes for nuclear and particle physics experiments. This laser ion source is based on high repetition rate titanium sapphire lasers. With these atoms can be element selectively excited and ionized. This allows for unprecedented purity of the extraced radioactive ion beams. Currently our research focuses on: (i) development of novel laser ionization schemes for elements, using auto-ionizing atomic states. (ii) improved laser designs for e.g. higher power, higher stabiity, (iii) push towards single atom detection sensitivity

Student roles:
Student is expected to learn the operation of experimental equipment and perform laboratory tasks after initial training independently. Will be working through a selection of supporting literature and textbook material, do literature research and give weekly presentations and progress reports on selected topics in atomic/molecular and optical physics topics related to the research performed. In parallel the student will experience work at an on-line isotope separator and accelerator facility. Supervised work with lasers and radiation areas may be required.

Skills required:
Interest in experimental work, techniques and instrumentation. Interest in operating experimental equipment.
Good documentation skills. A background in atomic/optical/nuclear physics and electronics or interest in these areas would be beneficial.
Student should be open to identify problems and be able to work independently and seek help from the group's PhD and MSc students as well as the group scientists as needed.

Research area: Our research group focuses on microfluidics and acoustofluidics — the study and control of fluids, particles, and microscale objects using acoustic waves in lab-on-chip devices. We investigate the fundamental physical mechanisms by which ultrasonic fields interact with microscale structures. These phenomena are harnessed to manipulate, propel, and characterize microrobots operating in liquid environments. Our experimental and numerical work aims to build a rigorous physical understanding of acoustically actuated systems at the microscale.

Student roles:
The intern will work in close collaboration with a doctoral student currently developing numerical and experimental tools for the characterization of acoustic microrobots. The primary responsibility of the intern will be to assist in building and running finite element simulations of the acoustic force and torque fields acting on microrobots of various shapes, using software such as COMSOL Multiphysics or a comparable platform. This will involve learning to set up acoustic-structure interaction models, meshing microrobot geometries, and post-processing simulation results to extract physically meaningful quantities such as acoustic radiation force and torque as a function of robot position and orientation.

The intern will also conduct a targeted review of the scientific literature on acoustic microrobot characterization and acoustofluidics, which will help contextualize the simulation results within the broader research landscape. Results will be compared systematically against analytical models and, where available, experimental measurements.

If the experimental setup is operational during the internship period, the intern may also assist the doctoral student with laboratory tasks, including the preparation of microfluidic chips, acoustic field characterization, and the tracking of microrobot trajectories under different acoustic excitation conditions.

Throughout the project, the intern will participate in regular group meetings, present progress updates, and contribute to the documentation of methods and results. The experience will provide solid training in computational physics, microfluidics, and scientific communication in a research environment. No prior experience with acoustic systems is required; motivation, attention to detail, and a strong background in physics or engineering are the primary assets sought. The intern will be supervised jointly by the doctoral student and the principal investigator.

Skills required:
- familiarity with the fundamentals of fluid mechanics and/or wave physics
- basic programming skills (Python, MATLAB, or equivalent) for data processing and post-processing of simulation results
- experience with or willingness to learn finite element simulation software (e.g., COMSOL Multiphysics)
- ability to read and synthesize scientific literature in English
- rigor, autonomy, and strong attention to detail
- prior exposure to microfluidics or acoustics is an asset but not required

Research area: This project is situated in the field of autonomous robotics, with a focus on adaptive path planning under uncertainty. It combines multi-objective optimization, risk-aware decision-making, and real-time mapping for robotic systems operating in complex environments. The research involves integrating advanced planning algorithms with robotic middleware (ROS2) and simulation tools (Unity), targeting applications in field robotics such as inspection, exploration, and others.

Student roles:
The student will extend and deploy the ARENA adaptive path planning framework on a new robotic platform. They will implement and adapt multi-objective optimization formulations to account for the platform’s dynamics, sensing modalities, and operational constraints. The work includes integrating mapping and planning components within a ROS2-based architecture, developing C++ modules, and ensuring real-time performance. The student will test the system in simulation (Unity) and evaluate robustness to uncertainty, dynamic environments, and sensing limitations. The student will benchmark different configurations of the planner, analyze trade-offs between objectives (e.g., safety, efficiency, information gain), and contribute to improving the generality of the framework across heterogeneous robots. The student will be part of a broader research effort on adaptive and risk-aware autonomy for field robotics applications.

Skills required:
Strong programming skills in C++ are required. Experience with ROS2 is essential. A background in robotics, control systems, optimization, or path planning is highly recommended. Familiarity with simulation environments (e.g., Unity) is an asset. The student should be comfortable working with complex software systems and have good problem-solving and debugging skills.

Research area: Our laboratory is examining all aspects of nuclear decommissioning from the early planning stages through characterization, modelling, decontamination, and dismantling. Our current focus is on the capture of radio-nuclides to reduce radioactive dose to the workers and the environment. We are working on different chemical and thermal based techniques for the removal of radio-nuclides from materials. While most of the work is experimental, we are also establishing databases and models related to nuclear decommissioning.

Student roles:
Modelling student is required to: (1) conduct a literature review related to concrete, (2) work with the team to establish the model parameters, (3) create a code to model the concrete, and (4) prepare documentation related to model development and simulation results. Specifically the student will focus on understanding the nature of concrete, its physical structure, and the forces that impact that structure. The work will include developing how the concrete will change over time, what the driving forces shall be, and what the final state of the concrete could be. Specific attention will be placed on changes to pore size, distribution, crack development and concrete strength.

Experiment student is required to: (1) conduct literature review on decontamination techniques, (2) manufacture concrete samples, (3) construct test rigs for testing, (4) apply decontamination methods to the concrete, and (5) prepare documentation related to the experimental procedures and experimental results.
In this work, the student will create different concrete samples which will include the different types of damage, different elements, and different nuclide species. The student will build a test section to hold the concrete samples will destructive testing is performed. This will include standard techniques of scabbling as well as new techniques developed in the lab. For those techniques not tested in the lab, a literature review would be conducted to help complete the database.

Skills required:
For the modelling portion of the project, the student would need decent coding skills. There already exists some models to work with but the student would need to develop new models in Python or a similar language and integrate them with existing codes. A general understanding of materials and mechanical stresses would be useful for this portion of the work.
For the decontamination work, the student would need basic laboratory skills and lab report writing skills.
Completion of 3rd year engineering and specifically any nuclear/radiation related courses would be an asset.

Research area: My research includes the study of aerodynamic and hydrodynamic flows, with emphasis on advanced simulation techniques, vortex dynamics, turbulence modeling, and wake control. I specialize in understanding and managing complex flow phenomena such as turbulence, separation, and wake interactions, which are critical for optimizing the performance of vehicles and vessels. Leveraging state-of-the-art CFD tools and high-performance computing (HPC) resources, I model, analyze, and enhance fluid performance in automotive, marine, and aerospace systems. My work aims to bridge theoretical insights with practical applications, providing innovative solutions to complex flow problems and advancing sustainable design practices.

Student roles:
The student will assist with the computational investigation of the aerodynamic wake behind a realistic vehicle model using CFD. Responsibilities include conducting literature reviews, preparing computational models and meshes, running numerical simulations as part of the research team, and post-processing simulation results to visualize flow structures and quantify aerodynamic performance. The student will analyze wake characteristics such as velocity deficits, vortex formation, and turbulence structures, document findings, and participate in regular research meetings. The intern will also contribute to the preparation of technical reports and presentations summarizing the project's outcomes.

Skills required:
The ideal candidate is a senior undergraduate student in Mechanical Engineering, Aerospace Engineering, or a closely related discipline with a solid background in fluid mechanics and engineering mathematics. Prior coursework in aerodynamics, computational fluid dynamics (CFD), or numerical methods is beneficial but not required. Experience with programming (e.g., MATLAB or Python) and scientific data analysis is considered an asset. The student should possess strong analytical and problem-solving skills, be comfortable working independently as well as in a research team, and have good written and verbal communication skills. Curiosity, motivation, and a willingness to learn advanced simulation techniques are highly valued.

Research area: In general, my research aims at increasing the sustainability and intelligence of the design, construction, and maintenance of infrastructure and buildings. It consists of three major components: 1) Application of AI technologies for design automation. 2) Application of computer vision and robotics to improve the safety and productivity of construction operations. 3) Infrastructure and building condition assessment using the Internet of Things with public participation (Crowdsensing-based infrastructure and building health monitoring).

Student roles:
1. Write the computer programs.
2. Implement deep learning algorithms using GPU under the guidance of graduate students and supervisor.
3. Write documents and reports in a professional fashion.
4. Communicate and collaborate with other researchers in the group effectively.
5. Work with other tasks assigned by the supervisor.

Skills required:
1. Knowledge of computer programming is a must.
2. Highly motivated, well-organized, and passionate about research with good communication skills in English.
3. Self-starter and motivator with the mindset of focusing on deliverables and getting the job done on time.
4. Experience in report writing and publishing is an asset.
5. Experience in machine learning is an asset.
6. Experience in vibe coding is an asset.

Research area: My research lies at the intersection of mechatronics engineering, artificial intelligence, and cardiovascular physiology. I develop hybrid solutions that combine physiological models and data-driven approaches to improve the monitoring of vital signs, predict the need for medical intervention, and optimize devices related to the cardiovascular system. Keywords: Arterial stiffness, Biosignals, Cardiovascular physiology, Control, Dynamic modeling, Machine learning, Mechatronics, Signal processing, System identification, Viscoelasticity

Student roles:
The intern will actively contribute to the development of a prototype cardiac-gated compression (CGC) device aimed at preventing episodes of orthostatic hypotension in patients with heart failure. Depending on their profile, they will be involved in the mechatronic design of the system, the integration of wearable physiological sensors, and the development of detection and adaptive control algorithms.
The intern will also participate in the development of experimental protocols, the collection of physiological data, and the analysis of hemodynamic responses in collaboration with clinicians.
This internship represents a unique opportunity to work at the interface of engineering, physiology, and medicine, within an applied research environment with strong clinical impact.

Skills required:
Must have at least one of the following skills:
- Mechatronic device design
- Mechanical design
- Electronics design
- Signal processing
- Mechanical modeling
- Data-driven modeling
- Machine learning

Research area: My research expertise spans several fields, including passive control of noise and vibrations, numerical modeling and simulation in vibroacoustics, robotics, artificial intelligence and production, and structure inspection. I explore ways to optimize the performance of mechanical systems using innovative techniques and by combining different disciplines. My objective is to contribute to creating sustainable and practical solutions to improve people's quality of life.

Student roles:
The intern will be responsible for defining the dominant vibrating systems in ships, such as propulsion engines, as well as identifying transfer paths and associated dominant frequencies. Using TPA engineering methods, they will propose solutions to minimize unwanted noise and vibration by modifying the source, receiving structure, and/or connecting joints. The intern will work collaboratively with the research team to achieve project objectives and present results in a clear and concise manner. They must possess a sensitivity to environmental issues related to ship noise and vibrations and their impact on marine mammals. Prior experience in vibration analysis and maritime structure design would be an asset.

Skills required:
We are looking for a resourceful, independent, rigorous, creative, and team-oriented intern. The candidate should possess knowledge in design (Catia, SolidWorks, etc.), modeling (Simcenter 3D, Femap, VA One, Nova, etc.), and programming (Matlab, Python, etc.). Previous experience in vibration analysis and maritime structure design would be a plus. The intern should also have sensitivity to environmental issues related to ship noise and vibrations as well as their impacts on marine mammals.

Research area: Mon domaine de recherche est axé sur le développement des dispositifs quantiques et photoniques avancés et leur intégration neuromorphiques dans des systèmes intelligents pour la surveillance en temps réel. En combinant les principes de la physique quantique, de l’ingénierie des matériaux, et de l’Internet des Objets (IoT), mes travaux visent à concevoir et fabrication des dispositifs de détection ultra-sensibles, capables de fonctionner dans des environnements complexes, notamment dans les secteurs de l’électronique de pointe et de la cybersécurité matérielle.

Student roles:
L’étudiant(e) recruté(e) jouera un rôle central dans la conception, l’implémentation et la validation d’architectures hybrides FPGA–neuromorphiques dédiées au traitement des signaux de capteurs quantiques. Son travail s’inscrira à l’interface entre électronique numérique, traitement du signal et intelligence neuromorphique.
Dans un premier temps, l’étudiant(e) réalisera une analyse approfondie des signaux issus des capteurs quantiques, en caractérisant leurs propriétés (faible amplitude, bruit, dérives) et en identifiant les défis associés à leur traitement en temps réel. Il/elle participera ensuite à la définition des algorithmes de traitement adaptés, en s’appuyant sur des approches classiques (filtrage, estimation) et neuromorphiques (réseaux de neurones impulsionnels, apprentissage en ligne).
L’étudiant(e) concevra et implémentera ces algorithmes sur des plates-formes FPGA, en optimisant les architectures pour répondre aux contraintes de latence, de précision et de consommation énergétique. Une attention particulière sera portée à la co-intégration avec des modules neuromorphiques, afin de développer des systèmes adaptatifs capables d’apprendre et de s’ajuster aux conditions d’acquisition en temps réel.

Skills required:
Le candidat idéal possède une formation en physique appliquée, génie électrique ou nanotechnologie, avec des connaissances en électroniques et/ou microélectroniques. Des compétences en programmation scientifique, modélisation, conception numérique et implémentation FPGA ou analyse de données expérimentales sont souhaitées. Le candidat doit faire preuve de rigueur scientifique, de curiosité intellectuelle et être capable de travailler de manière autonome tout en collaborant au sein d’un environnement de recherche multidisciplinaire.

Research area: Focusing on the interface between energy and mining engineering, I aim to tackle today’s climate and energy challenges by decarbonizing energy systems in mines. Research in my lab develops and implements clean energy technology, accelerating towards a sustainable future for the mining industry. It advances the areas of mine electrification, mine ventilation, heating and cooling, renewable energy, and energy storage.

Student roles:
Milestone 1: Understand fundamentals of freezing process and complete literature review.
Milestone 2: Develop a multiphysics model via COMSOL for AGF and verify/validate it with/against existing data.
Milestone 3: Assist the creation of novel physics-based correlations, while considering various scenarios due to climate change.
Milestone 4: Produce a final report and present findings.

Skills required:
- Solid Mechanics I (or equivalent)
- Fluid Mechanics (or equivalent)
- Skills in FEM/FVM software (or willing to learn)

Research area: The HeART (Healthcare Applications for Robotic Technologies) Lab is focused on the fundamental understanding and development of magnetically actuated small-scale robots for surgical and onchip applications. This involves investigating the design, fabrication, and control of these robotic devices. HeART Lab director Dr. Onaizah has been involved in the development of the first shapeforming magnetic continuum robots to navigate along tortuous anatomical paths and experimentally demonstrated this in realistic in vitro environments. The HeART works closely with clinicians to solve some of the most challenging problems at the forefront of medical robotics.

Student roles:
As a regular part of the team, the intern will be given a desk in one of the team offices. They will be integrated into a group of around 10 students working directly on medical robotics, under the direct supervision of Dr. Onaizah. In addition to this group, the intern will be a regular member of the HeART Lab, and benefit from its associated research ecosystem (e.g., seminars, interactions with other students, facilities). The intern will progressively take ownership of their project using a rampup approach. The first couple of weeks of the internship will be dedicated to understanding the research task ecosystem, and the different paths they can take in this context. Paired to a senior member of the team (PhD or Masters' student), careful analysis, reviews and guidance will be provided during the ramp-up period, so that eventually the intern can become more independent and can take initiative on their project. When reaching this point, an iterative and incremental approach will be used to tackle the research objective of this internship: a research question will be designed with their immediate supervisor, and the associated research will be done to tackle this question (including literature review, design analysis, experiments, coding, empirical analysis, when relevant to the research question). Finally, the obtained results will be presented to the other members during a group meeting, and the next steps and research directions will be discussed collectively.

Skills required:
Minimum Requirements:
• Background in one of the following disciplines: Mechanical Engineering, Biomedical Engineering,
Electrical Engineering, Robotics, Computer Science, or related disciplines
• A willingness to work effectively with staff, students, and visitors from a wide range of
backgrounds.
Experience in one of the following areas would be an asset:
• Robot Operating System (ROS) and its Gazebo simulation package
• Object-oriented programming language (e.g., embedded C, Matlab, Python, C++, Java, or
LabVIEW)
• Computer Aided Design software (e.g. Solidworks, AutoCAD)
• Computer Aided Manufacturing (3D printing, laser cutting, CNC milling)
• Magnetic Control or Manipulation
• Microfabrication

Research area: I am interested in solid waste management. My recent projects at the Waste Management System Design Laboratory (WMSD Lab) focus on (i) waste generation and recycling behaviors during COVID-19, (ii) the use of remote sensing and satellite imagery in waste management applications, (iii) food waste and textile waste management, and (iv) low-level radioactive waste management. I have received awards in both teaching and research, and I am a Canada Research Chair (Tier 1) at the University of Regina. All my former Mitacs GRI students have enjoyed their internships with us. Check out our LinkedIn page.

Student roles:
I am an award-winning teacher and researcher at the University of Regina, and I would like to provide you with training on the following research skills: to identify good research questions, to gather and verify good data, to generate hypotheses, to conduct laboratory work, to develop models and to provide theorems, to make predictions and conclusions, and to solve practical problems using engineering tools. Typically, the student researcher will work on the following during their 12-week stay with us: (i) to conduct an effective literature review, (ii) to conduct laboratory work with various technical and statistical analyses, and (iii) to prepare technical reports.

The student researcher will prepare weekly presentations in front of other Master's and Doctoral students (the “Research Group Meetings”), and draft scientific papers. Outstanding candidates will be invited to serve as co-authors on technical publications. Scientific publishing will jump-start your professional career. In fact, many of my former Mitacs GRI students have published their first peer-reviewed journal manuscripts under my supervision. Please contact me for samples.

Please check my website (http://uregina.ca/~ng224/) for more on my teaching philosophy statement and past projects. Contact me if you would like to join us in solving the world’s waste problem. Waste Management System Design (WMSD) Laboratory at the University of Regina is partially funded by a Canada Foundation for Innovation John R. Evans Leaders Fund (CFI-JELF) grant and is a private research lab. The space is for trainees under the supervision of the Canada Research Chair (Tier 1) in Environmental Sustainability. Equity, Diversity, and Inclusion (EDI) is important to the success of my research work. A diverse workplace is a strength, and it requires awareness and sensitivity to cultural norms. Please be mindful of cultural differences.

Skills required:
The student should have an excellent background in Civil / Environmental engineering, with proper laboratory health and safety training. The student must be curious and inquisitive. He/she must be interested in sustainable solid waste management and be prepared to work with waste sampling and quantification. The ideal candidates should have excellent numerical modeling and laboratory skills. Effective communication skills (both oral and written) are expected. The student researcher should be able to work independently and collaboratively. Field experience with data collection, sampling, and materials characterization is an asset.

Research area: Partial Differential Equations; Applied Analysis.

Student roles:
Perform analytical calculations; Conduct numerical experiments.

Skills required:
Solid foundation in multivariable calculus, ordinary differential equations, and linear algebra. Basic programming skills.

Research area: The Advanced Control and Intelligent Systems (ACIS) Laboratory focuses on cyber-physical systems, autonomous robotics, and automated industrial inspection. Our research spans multi-robot collaboration, digital twinning, and autonomous unmanned aerial vehicle (UAV) payloads. We harness 3D computer vision, machine learning, and embodied AI to transition robotic platforms from controlled environments to real-world, uncertain conditions.

Student roles:
The intern will act as a software developer and data engineer. They will be provided with pre-existing 3D scene data and text documents. The student will write scripts to extract entities and relationships from the text to populate the Knowledge Graph. Simultaneously, they will work with lab members to extract spatial relationships from the 3D data to build the Scene Graph. The core of their summer will be spent developing the cross-referencing logic by writing queries and deploying language models to compare the two graphs. They will design a pipeline that takes a user query (e.g., "Are the structural supports compliant?") and automatically generates a text-based response and draft report based on the graph analysis.

Skills required:
Applicants should have a background in computer science, software engineering, or data science. Proficiency in Python is mandatory. The ideal candidate will have experience with Natural Language Processing (NLP) techniques, Large Language Models (LLMs), and graph databases (e.g., Neo4j). A basic understanding of 3D data structures or computer vision is a strong plus.

Research area: The Advanced Control and Intelligent Systems (ACIS) Laboratory focuses on cyber-physical systems, autonomous robotics, and automated industrial inspection. Our research spans multi-robot collaboration, digital twinning, and autonomous unmanned aerial vehicle (UAV) payloads. We harness 3D computer vision, machine learning, and embodied AI to transition robotic platforms from controlled environments to real-world, uncertain conditions.

Student roles:
The intern will be a core contributor to the lab's aerial inspection team. Over the three months, they will conduct a literature review on current best-view planning and path-planning methodologies for UAVs. They will implement selected algorithms within a simulation environment (such as Gazebo) to test autonomous navigation logic. Following successful simulation, the intern will assist the ACIS team in deploying these algorithms onto our physical drone payload hardware for field testing. The student will be responsible for processing the collected visual data, evaluating the efficiency of the edge-computing pipeline, and documenting their code and findings.

Skills required:
Applicants should have a strong background in computer science, software engineering, or robotics. Proficiency in Python is required. Familiarity with the Robot Operating System (ROS/ROS2) and basic 3D computer vision concepts is highly desirable. Experience with drone hardware or path-planning algorithms is an asset but not strictly required.

Research area: Autonomous surface vehicles (ASV), ROS 2, aquatic robot simulation, path planning, obstacle avoidance, computer vision, invasive aquatic species detection, deep learning

Student roles:
The student will:
1. Conduct a literature review on autonomous surface vehicles (ASVs), aquatic robot navigation, and vision-based aquatic plant detection.
2. Set up and configure a ROS 2-compatible aquatic simulation environment and model the BlueBoat vessel.
3. Design and implement an autonomous navigation stack: area coverage, path planning, and obstacle avoidance on open water.
4. Integrate a lightweight deep learning detection model to identify invasive aquatic plant species from camera imagery.
5. Validate the complete system in simulation, then participate in field trials with the BlueBoat on a lake in the Estrie region.
6. Write a final technical report documenting the architecture, results, and recommendations for future development.

Skills required:
Required:
- Experience with ROS 2 (nodes, topics, services, launch files)
- Programming in Python and/or C++
- Familiarity with robot navigation concepts (path planning, obstacle avoidance, localization)
- Ability to work in a Linux environment
- Autonomy and scientific curiosity

Strong assets:
- Prior experience in aquatic robotics or simulation of marine/surface vehicles
- Experience with robot simulation environments (Unity, IsaacSim, or equivalent)
- Knowledge of computer vision and deep learning (PyTorch or similar)
- Experience with sensor integration (GPS, IMU, cameras, LiDAR)

Research area: Our research focuses on keeping our cities safe by monitoring the health of aging civil infrastructure, like concrete bridges. Over time, these structures weaken due to age and harsh weather. Instead of sending human inspectors into dangerous situations, we use camera-equipped drones (UAVs) and artificial intelligence to automate the inspection process. By combining drone imagery with AI and 3D modeling, we create highly accurate virtual copies of bridges. This modern approach helps us find damage faster, track it over time, and make better decisions to repair and maintain our vital transportation networks.

Student roles:
As a Globalink Research Intern, you will be a key part of our research team, working to make bridge inspections smarter and safer. Your primary role will involve working with autonomous drone navigation systems and the visual data collected from flights over real-world bridges. Your tasks may include:

- Flight Automation: You will assist in testing and simulating smart navigation routes, helping to evaluate how safely and efficiently autonomous drones can fly around complex structures.
- Working with AI: You will help train and test artificial intelligence models to automatically spot cracks and damage in pictures of concrete structures.
- Building 3D Models: You will use specialized software to turn 2D drone images into immersive 3D digital models of the bridges we inspect.
- Data Analysis: You will help analyze how bridge cracks grow over time by comparing visual data from different drone flights.
- Team Collaboration: You will document your progress, synthesize your results, and share your findings during our weekly team meetings.

These roles are designed to be a learning experience. You will be guided by experienced researchers and gain practical, hands-on experience applying modern AI, robotics, and computer science tools to solve real-world civil engineering problems.

Skills required:
We are looking for motivated students with a background in civil engineering, computer science, or a related technical field. You should have some basic programming experience, ideally in Python. Familiarity with machine learning and deep learning libraries like PyTorch is a bonus. You do not need to be an expert in artificial intelligence, but an interest in computer vision, machine learning, or 3D modeling is highly desired. The most important skills are a strong willingness to learn, good problem-solving abilities, and the excitement to work on a hands-on project that blends traditional engineering with modern computer science.

Research area: In the Adaptive Bionics Lab, we research the design of quasi-passive prostheses and exoskeletons that adapt to speed, terrain, and ground surface for walking and running optimization. Our research merges precision machine design, biomechanics, and robotics for the development of new types of prostheses and exoskeletons and evaluation of gait. Within this largely understudied area of wearable robotics research, we develop devices that adjust joint stiffness, position and/or dampening in order to mimic important physiological behavior of biological lower limbs. We study the biomechanics of human gait and how our developed devices may improve walking.

Student roles:
The Mitacs Globalink intern will collaborate with researchers in the lab on this project, with a focus on device characterization and evaluation. The student will conduct benchtop experiments, collect data, analyze data, and interpret results. The student may also assist with biomechanical evaluation with human participants using motion capture and force plate analysis, and assist with processing the collected data. The intern will be exposed to the tools and methods we use in the lab, including metabolic analysis, motion capture, biomechanical evaluation, material characterization, and finite element analysis.

Skills required:
This project is suitable for a student with a strong background in mechanical engineering and design, and an interest in biomedical applications. A strong foundation from undergraduate engineering courses in physics, dynamics, and statics is ideal. The student should be familiar with reading scientific articles in engineering or medicine. Prior experience in Matlab and Solidworks or other computational and modeling software is strongly preferred. A successful student should be able to work independently with guidance and mentorship from graduate students and professors.

Research area: Biopolymers, biopolyols, bio-based polyurethanes

Student roles:
− Conduct controlled epoxidation of plant oils and oil blends using in situ generated peroxyacids under established reaction conditions.
− Perform hydroxylation of epoxidized oil derivatives to synthesize biopolyols through acid-catalyzed epoxy ring-opening reactions with alcohols.
− Characterize reaction products using standard analytical methods and techniques.
− Assist with reaction kinetics studies and process optimization
− Maintain accurate experimental records and contribute to technical reports.

Skills required:
The successful candidate should be enrolled in a program in Chemistry, Chemical Engineering, Materials Science or a related discipline. Candidates should be comfortable working in a lab environment and demonstrate strong analytical skills.

Research area: Prof. Amar Mohanty is an internationally recognized leader in sustainable materials, bioplastics, biocomposites, and circular economy innovations. His research focuses on developing high-performance, value-added materials from biomass residues, industrial coproducts, and post-consumer waste streams while promoting environmentally sustainable manufacturing practices. His work spans biocarbon-reinforced composites, waste plastic valorization, sustainable alternatives to single-use plastics, and advanced materials for additive manufacturing and automotive applications. More recently, his research integrates artificial intelligence (AI) and machine learning (ML) tools to accelerate material design, process optimization, performance prediction, and sustainable product development, supporting the transition toward next-generation circular and low-carbon material systems.

Student roles:
The project is an opportunity to be involved in cutting-edge research activities and learn the basics of polymer science and engineering, including processing and characterization using state-of-the-art equipment in the Bioproducts Discovery and Development Centre (BDDC), at the University of Guelph. Undergraduate students in this role will be required to:
1) Apply principles of scientific research, working in a team of researchers;
2) Adhere to safe and proper lab practices;
3) Collect and analyze data;
4) Apply critical and analytical thinking;
5) Gain hands-on experience in polymer processing and characterization techniques, including extrusion, injection molding, thermal analysis, mechanical testing, and materials characterization;
6) Practice written and oral scientific communication (progress reports, scientific publications, weekly group meetings and presentations at events).

Skills required:
Undergraduate students in engineering, physics, chemistry, or other related fields of study with some lab experience through classes or the workplace.
• Highly motivated to learn to prepare and test samples and operate minor lab equipment independently.
• Courses taken in Materials Science are highly desirable.
• Knowledge in polymers, fibers, and composites is an advantage.
• Excellent computer skills (Microsoft Office: Word, Excel, PowerPoint, etc.).
• Excellent communication skills (oral and written communication).
• Detail-oriented data recording.
• Diligent in maintaining safe work protocols.
• Knowledge of machine learning is an added advantage.

Research area: My specialized area is human movement research - more specifically biomechanics, motion capture, and gait analysis research. We use a 12 camera Vicon motion capture system to track body movement during walking. These infrared cameras track reflective markers located on the skin. Kinematic and kinetic data is computed and used to identify movement deviations. Biomechanical signals are processed using python and Visual 3D software. This allows for a greater understanding of atypical and typical motion in the body. In turn, we can increase our understanding of disorders, treatments, and try to improve function and quality of life.

Student roles:
The student will be responsible for retroactively analyzing biomechanical input data using machine learning classifiers to identify major determinants of gait patterns, classification of gait patterns, gait data reduction, determination of optimal gait/balance variables, interpretable findings, etc. Brief weekly reports of progress are required. Students will contribute to conference and journal article publications.

Students will gain valuable hardware and software skills in motion capture technology and machine learning. Students will have the opportunity to work with patients during data collection as well. Students will be trained in complex biomechanical and AI analyses with an emphasis on data quality and interpretability for healthcare applications.
We have great student teams here each year from all over the world - come join us!

Skills required:
Advanced machine learning knowledge (classifiers, discriminatory algorithms)
Good python programming ability
Knowledge of neural networks, deep learning

Research area: Dr. Tate Cao’s research specializes in engineering entrepreneurship education, innovation ecosystems, and technology commercialization, with a focus on how academic programs can effectively develop entrepreneurial mindset and practice among engineering students. His work integrates program design, curricular and co-curricular alignment, and experiential learning models, particularly within deep-tech and applied innovation contexts. He also examines assessment frameworks for entrepreneurship education, including both traditional venture outcomes and broader, long-term impacts such as career trajectories and innovation capacity. Complementing this, his applied research spans industry-collaborative innovation projects in areas such as ag-tech, biomedical engineering, and digital systems, linking education with real-world commercialization pathways.

Student roles:
Students will serve as research assistants contributing to all phases of the project. Their responsibilities will include:

Landscape Analysis and Data Collection
Students will identify and compile a list of universities with notable engineering entrepreneurship programs. They will gather relevant information from websites, course catalogs, program materials, and publicly available reports. This includes mapping curriculum requirements, co-curricular offerings, and program pathways.

Program Mapping and Comparative Analysis
Students will develop structured frameworks to compare program designs across institutions. They will categorize program elements (e.g., courses, incubators, competitions) and analyze how institutions integrate these components. Students may also contribute to creating visual diagrams illustrating program structures and student journeys.

Qualitative Research Support
If interviews or case studies are conducted, students will assist in preparing interview protocols, taking notes, and summarizing insights. They may also help identify themes and patterns across qualitative data.

Synthesis and Reporting
Students will contribute to drafting sections of the final report, including literature synthesis, case summaries, and recommendations. They will help translate findings into actionable insights for program design and strategic planning.

Presentation and Deliverables
Students will assist in developing presentation materials (slides, infographics) to communicate findings to stakeholders. They will also participate in periodic progress reviews and a final presentation summarizing key outcomes.

Through this work, students will gain hands-on experience in education research, program design, and strategic analysis within entrepreneurship ecosystems.

Skills required:
Students should have an interest in entrepreneurship, innovation, or engineering education. Ideal candidates will have backgrounds in engineering, business, education, or related fields. Strong analytical, writing, and organizational skills are important, along with the ability to synthesize information from diverse sources. Experience with qualitative research (e.g., interviews, thematic coding) or benchmarking studies is helpful but not required. Students should be comfortable working independently and collaboratively, and willing to engage with complex educational systems and institutional data. Familiarity with startup ecosystems or participation in entrepreneurship programs is a plus.

Research area: Dr. Domenico Santoro's research focuses on advanced water treatment technologies, particularly in disinfection, biosolids treatment, and resource recovery. He specializes in applying computational fluid dynamics (CFD) and multi-scale modeling to optimize water treatment processes, including advanced oxidation for wastewater reuse. Dr. Santoro leads international collaborations with academic and industrial partners to innovate in water treatment technologies. His work spans areas such as ozonation, biosolids processing, and the development of sustainable water treatment practices. He is also involved in the development of patents and optimization strategies for water treatment systems .

Student roles:
1. Analytical Chemistry (1 Student)
The student in this role will focus on analyzing nutrient removal efficiencies in wastewater using solid-state carbon materials (e.g., biochar, activated carbon). They will conduct experiments using spectrophotometry, ion chromatography, and HPLC to quantify nitrogen and phosphorus levels before and after treatment. Their responsibilities include sample preparation, setting experimental conditions, and ensuring proper data collection and interpretation. The student will assess the efficiency of various carbon materials for nutrient adsorption, focusing on their adsorption capacities and stability. They will collaborate with other project members to interpret results and suggest improvements for enhanced nutrient management.

2. Chemical/Environmental Engineering (1 Student)
This student will design and conduct experiments to evaluate the performance of solid-state carbon materials for nutrient removal in wastewater. Their role includes setting up and optimizing pilot-scale systems, adjusting contact time, carbon dosage, and reactor conditions. The student will investigate the regeneration potential of carbon materials to ensure long-term sustainability. They will also focus on process optimization to maximize nutrient removal efficiency while minimizing costs. The student will contribute to experimental setup, troubleshooting, and data analysis, ensuring results are reliable for scaling up the treatment process and enhancing overall system performance.

3. Physics/Applied Mathematics (1 Student)
The intern in this role will develop mathematical models to simulate nutrient adsorption processes on solid-state carbon materials. Using tools like MATLAB or Python, they will model adsorption kinetics, diffusion, and mass transfer, optimizing experimental parameters for better nutrient removal. They will analyze data through computational models, validating experimental results and providing recommendations for improving efficiency. This student will apply principles of physics and mathematics to enhance understanding of the treatment system and assist with process optimization, contributing to a more effective and cost-efficient nutrient management solution.

Skills required:
Analytical Chemistry (1 student): Expertise in nutrient analysis (nitrogen, phosphorus) and solid-state carbon characterization.

Chemical/Environmental Engineering (1 student): Knowledge of adsorption processes, biological nutrient removal, and pilot-scale system operation.

Physics/Applied Mathematics (1 student): Proficiency in modeling adsorption kinetics and nutrient removal. Experience with MATLAB or Python for simulations.

Research area: At the Robotics and Mechanisms Laboratory we concentrate on the novel design of manipulators. We are mostly interested on parallel manipulators (robotic manipulators with a parallel structure). In the last few years we have concentrated on cable-driven parallel manipulators (CDPM). CDPMs are parallel robots whose 'legs' are cables as opposed to rigid links. We are currently working on enhancing CDPMs by adding one or more extensible rigid struts that would be able to push thus greatly extending the manipulator's workspace.

Student roles:
The student will be in charge of detailed design, construction and testing of single degree of freedom high packing ratio linear actuators. Tasks will include modelling in CAD software, mechanical fabrication, assembly, determining specifications of purchased items, ordering items, implementing actuators and sensors, testing and documentation.

The student will build on the work currently underway at the Robotics and Mechanisms Laboratory at UNB.

Skills required:
The student should be a senior undergraduate students or masters student in Mechanical Engineering, Mechatronics Engineering or related discipline. The successful candidate would have excellent working knowledge of computer aided design (CAD), mechanical design as well as traditional fabrication techniques.

The candidate should also have working knowledge in the implementation of electromechanical devices including motors, sensors, control systems and related hardware.

Research area: Contrôle passif du bruit et des vibrations Modélisation et analyse numérique en vibroacoustique Robotique Intelligence artificielle et productique Surveillance de structures

Student roles:
Appliquer la méthode IWM pour des structures courbes.

Déduire l’amortissement structural.

Skills required:
Débrouillard, indépendant, rigoureux, créatif, avoir un bon esprit d’équipe.
Connaissance en Conception (Catia, SolidWorks, etc..), Modélisation (Simcenter 3D, Femap, VA One, Nova, etc..), Programmation (Matlab, Python, etc..), Intelligence artificielle (analyses des données).

Research area: My field of research is geomatics engineering. My projects focus specifically on mobile LiDAR systems, which provide large-scale, accurate and affordable 3D point clouds. They are used in applications such as driverless vehicles, land-use planning and, more recently, for the creation of digital twins city. This is a virtual representation of a city's physical assets (e.g. roads; buildings; vegetated areas; ...) throughout their lifecycle. My project research objectives mainly concern the semantic segmentation of point clouds, notably using artificial intelligence approaches, and 3D modeling.

Student roles:
The intern’s work will consist of developing image-based change detection methods. Both image processing techniques and artificial intelligence models available in smart cameras may be utilized. The work will also explore multi-view 3D reconstruction based on the acquired images. Gaussian Splatting techniques may be investigated to assess their effectiveness in the context of the miniature environment. We will also focus on 3D modeling itself in order to create a digital 3D model of the main objects of interest present in the environment. All of this work will be carried out using Python programming and open-source libraries and code (OpenCV; scikit-learn; pyTorch; …). It will build upon the work already carried out by other students and interns in the laboratory.

Skills required:
Good knowledge and skills in image processing or computer vision
Knowledge of machine learning
Very good knowledge and skills in Python programming
Knowledge in the following areas would be an asset: deep learning; deep neural networks; geomatics; photogrammetry; Linux environment
Resourcefulness, ability to solve technical problem and creativity
Good communication skills to technical and non-technical communities, both verbal and writing
Ability to work both individually and as a team member

Research area: My research focuses on the design, characterization, and reliability assessment of semiconductor devices and integrated circuits for harsh-environment applications. The work spans analog and mixed-signal microelectronics, wide-bandgap semiconductor technologies such as gallium nitride (GaN), and radiation-tolerant electronics for aerospace and scientific instrumentation. Research activities include device modeling, circuit design, experimental characterization, and the development of robust electronic systems capable of operating under extreme temperature and radiation conditions.

Student roles:
The student will work as part of a research team under the supervision of graduate researchers and the principal investigator. The primary role will be to support characterization and testing of semiconductor devices and integrated circuits.
Responsibilities may include:
- Assisting with the setup and operation of laboratory measurement equipment.
- Supporting the execution of electrical characterization experiments.
- Collecting, organizing, and processing experimental data.
- Performing preliminary analysis of measurement results.
- Assisting with the preparation of test procedures and measurement documentation.
- Maintaining organized records of experiments and characterization results.
- Participating in technical meetings and discussions related to experimental activities.
- Contributing to the preparation of technical reports and research documentation.
Through these activities, the student will develop practical skills in semiconductor characterization, laboratory measurements, data analysis, and engineering research while contributing to ongoing research efforts in advanced microelectronics for harsh-environment applications.

Skills required:
The student should be enrolled in Electrical Engineering, Computer Engineering, Engineering Physics, Physics, or a related discipline and have a solid background in semiconductor devices and microelectronics. The candidate should possess knowledge of electronic circuits, semiconductor device operation, and circuit simulation techniques. Experience with Cadence Virtuoso for schematic entry and circuit simulation is highly desirable. Familiarity with laboratory measurements, data analysis tools such as MATLAB or Python, and experimental characterization methodologies is an asset. The student should demonstrate strong analytical and problem-solving skills, attention to detail, and a strong interest in semiconductor technologies and harsh-environment electronics.

Research area: A research study to investigate risk factors involved with the airborne transmission of viral-laden droplets in indoor environments can assist in determining and analyzing the effectiveness of methods for minimizing outbreaks due to airborne transmission. The opportunity will involve collaborating with a graduate student to design and conduct numerical analyses models for studying droplet dispersion patterns in indoor environments considering the implications of HVAC systems. Use and further development of heated manikins, control systems, and aerosol measurement experimental equipment will be used.

Student roles:
You will conduct CFD analyses, practice your oral communication skills by giving an oral presentation. The output of your research will also be to develop a manuscript to fully detail the modeling process and various case studies, and to conduct experimental aerosol studies.

Skills required:
Familiarity with Solidworks, ANSYS FLUENT, MATLAB, computer programming, modeling and basic engineering skills are required. Computational Fluid Dynamics (CFD) and solid modelling experience is an asset. Training will be provided through weekly meetings and guidance, including training in ANSYS, Matlab, literature reviews and academic writing throughout the project.

Research area: I work at the interface between statistical physics, control theory, and machine learning. My goal is to understand better the role that information theory plays in feedback and control. I do this using tools from both control theory and physics. The latter involves stochastic thermodynamics, a field that mixes information theory, thermodynamics, and nonequilibrium statistical physics. I am particularly interested in the relations between information and control and what makes control robust. I also study other connections between control theory and physics, such as the physical limits to control and the role of causality.

Student roles:
I run a very “hands-on” lab and spend a lot of my time talking to the students. I will participate directly in the supervision (with help from grad students and/or postdocs in the group) and look forward to lots of interactions. Your role:

1) To learn some relevant background in control theory and statistical physics — as needed.

2) To learn basic programming skills (for example, the real-time control software we use, plus a high-level language such as Python or Matlab or Julia or C, necessary for the project.

3) To do occasional presentations on your project and your progress in our weekly group meetings. Typically, students make a couple of presentations over the course of an internship. These let other students know about your work but also help in your own understanding of the project and help to build your presentation skills.

4) The physical apparatus may need modification. If so, we will offer the necessary background to use the required machine tools safely. (We offer machine-shop courses taught by experienced machinists, if that is necessary.) Some parts may be manufactured using 3d printing, too.

5) Your daily routine may include simulation studies and / or work on real hardware. You will learn how to simulate control systems and gain actual hardware experience, too.

Skills required:
A perfect background would include courses in control theory, linear systems, statistical physics, and a bit of machine learning. You should also have good programming skills in some language (Python, Mathematica, C, LabVIEW, Matlab, Julia, etc.). Basic lab skills (elementary circuits, data acquisition) are also be desirable. As no one knows all this, I will be looking for people with some of the background and – most important – who are enthusiastic and can learn quickly. You should want to know WHY and not just want to “use formulas.”

Research area: Our research focuses on developing sustainable and circular solutions to global waste and energy challenges. We specialize in the thermochemical conversion of waste materials, including plastics, biomass, and industrial byproducts, into valuable resources such as fuels, chemicals, and adsorbents. Using technologies like pyrolysis and gasification, we investigate how to optimize process conditions and catalyst use to maximize product yield and quality. Our goal is to reduce environmental burdens while promoting resource recovery through advanced chemical engineering approaches, contributing to cleaner energy systems and a more sustainable future.

Student roles:
The student will be involved in all key stages of the pyrolysis process, starting with the preparation and characterization of plastic waste feedstocks. The initial phase will include sorting and shredding plastic samples, selecting representative types (e.g., HDPE, LDPE, PP), and documenting their physical and chemical characteristics.
Next, the student will assist in operating a bench-scale pyrolysis reactor, conducting experiments under varying conditions to determine how temperature, heating rate, and residence time influence product distribution. Selected experiments will include catalytic pyrolysis using solid catalysts (such as zeolites or metal oxides) to improve the selectivity and energy content of the bio-oil.
After each run, the student will collect, separate, and analyze the resulting products—liquid (bio-oil), gas, and char. The bio-oil will be analyzed using techniques such as gas chromatography-mass spectrometry (GC-MS), Fourier-transform infrared spectroscopy (FTIR), and calorific value testing to determine its composition and energy content. The student will record and interpret the data to evaluate which processing conditions produce the highest yield and most desirable product qualities.
Throughout the project, the student will receive training in laboratory safety, data handling, and scientific reporting. They will also participate in research meetings and collaborate with graduate students working on related topics. By the end of the internship, the student will have gained practical skills in thermochemical processing, environmental engineering, and waste-to-energy conversion, plus the satisfaction of contributing to a sustainable solution for plastic pollution.

Skills required:
We are looking for motivated students from chemical engineering, environmental engineering, or materials science backgrounds. Experience with thermal or chemical processes, laboratory work (especially handling organic compounds or thermal reactors), and basic data analysis is highly desirable. Proficiency in Microsoft Excel or other data tools is required. Knowledge of reaction kinetics or thermodynamics is an asset but not mandatory. Students should be curious, safety-conscious, and capable of working both independently and as part of a team.

Research area: There is an urgent need for an alternative to landfilling wastewater biosolids, plastics, and textiles. We are developing an integrated system that converts high volume, ecologically damaging waste streams into valuable chemicals and fuels through pyrolysis and anaerobic fermentation. Pyrolysis, a high-temperature process, converts feedstocks (e.g. biosolids, plastics) into bio-oil, non-condensable gases, and biochar. Bio-oil can be further transformed into low-carbon fuels. Microbes can ferment non-condensable gas into medium chain fatty acids (MCFA), precursors for high-value chemicals like biojet. The research program is an interdisciplinary collaboration of thermochemical and biological specialists.

Student roles:
The Mitacs student will plan experiments with supervisor's help, conduct reactions, run analytical procedures such as gas and liquid chromatography, and maintain a detailed lab book. Experimental work may require collaborating with graduate students in Trajano lab as well as other UBC labs. The student will be responsible for processing raw data and interpreting results within the context of previous studies using software such as Excel, Matlab or Python. Student will develop a model to describe experimental data. Each week the student will attend group meeting and present a progress report or a discussion of a recent journal article. Student will complete safety training and follow all safety protocols. Additional tasks (such as repairing equipment) may be necessary.

Skills required:
Chemical engineering background is preferred, related degrees are welcome. Familiarity with mass balances, mass transfer, and kinetics is helpful. Experience safely operating reactors, and analytical systems (e.g. liquid chromatography) is advantageous but not mandatory. Previous experience with Excel, MatLab, or Python is desired. The student should be self-motivated and capable of working independently as well as part of a team. The student must be able to identify, prioritize, and complete tasks to reach the desired outcome in a safe, efficient, and innovative way. The student should be curious and have strong reasoning skills. Strong communication skills, especially written, are critical.

Research area: We are engaged in developing new artificial intelligence techniques to reduce the prospects for cyber-interference in spacecraft

Student roles:
The student will demonstrate the basic mechanics of integrating symbolic AI with connectionist AI in a simple task typical of a spacecraft.

Skills required:
This is a software project for students with a strong interest in AI, especially expert systems, Bayesian networks and neural networks (with perhaps some FPGA experience)

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser biostimulation to improve seed viability and resilience.

Student roles:
Up to 2 interns may participate in this project, each working on one crop type. The role of each student includes
1. Preprocess and organize X-ray image datasets [1].
2. Apply classical data augmentation techniques.
3. Develop and train GAN models using Python or Matlab.
3. Train and evaluate CNN classifiers using both real and GAN-generated data [2].
5. Documentation and reporting [1-3].
References:
[1] Nadimi, M., Divyanth, L. G., & Paliwal, J. (2023). Automated detection of mechanical damage in flaxseeds using radiographic imaging and machine learning. Food and Bioprocess Technology, 16(3), 526-536.
[2] Divyanth, L. G., Guru, D. S., Soni, P., Machavaram, R., Nadimi, M., & Paliwal, J. (2022). Image-to-image translation-based data augmentation for improving crop/weed classification models for precision agriculture applications. Algorithms, 15(11), 401.
[3] Nadimi, M., Loewen, G., & Paliwal, J. (2022). Assessment of mechanical damage to flaxseeds using radiographic imaging and tomography. Smart Agricultural Technology, 2, 100057.

Skills required:
The student should have a background in engineering, computer science, agriculture or a related field. Basic skills in programming (preferably MATLAB or Python) and data analysis are needed. An interest in imaging, sensors, or agriculture is helpful. The student should be willing to learn, able to follow experimental procedures, and work well both independently and in a team. Previous experience with image processing, machine learning, spectroscopy or lab work is a bonus.

Research area: My research focuses on artificial intelligence, computer vision, and computational medical imaging, with applications in cancer imaging, radiomics, digital pathology, and multimodal biomarker discovery. The lab develops machine learning and deep learning methods for analyzing medical images, including CT scans, whole-slide pathology images, and spatial/molecular imaging data. A particular focus is on building interpretable AI models that can identify clinically meaningful imaging patterns related to disease detection, progression, recurrence, and treatment response.

Student roles:
The student will contribute to the design, implementation, and evaluation of an anomaly detection pipeline for lung CT imaging. Their responsibilities will include reviewing relevant literature, organizing and preprocessing de-identified CT image data, implementing baseline models, training and testing anomaly detection algorithms, and visualizing model outputs. The student will begin by learning the basics of lung CT image analysis, including DICOM/NIfTI formats, voxel spacing, lung windowing, intensity normalization, and simple lung or nodule region extraction. They will then implement baseline anomaly detection approaches, such as reconstruction-based autoencoders and feature-space outlier detection. Depending on progress, the student may also explore more advanced self-supervised or transformer-based approaches. The intern will analyze experimental results using quantitative metrics and qualitative visualizations, including anomaly heatmaps overlaid on CT slices. They will meet regularly with the supervisor and lab members, participate in research discussions, and present progress during lab meetings. By the end of the internship, the student is expected to produce clean, documented code, a summary report, and a final presentation describing methods, experiments, results, limitations, and future directions.

Skills required:
The student should have a strong background in programming and machine learning, preferably using Python and PyTorch or TensorFlow. Familiarity with computer vision, medical imaging, image processing, statistics, or deep learning is an asset. Experience with NumPy, pandas, scikit-learn, OpenCV, SimpleITK, MONAI, or 3D image analysis would be helpful but is not required. The student should be motivated, independent, and interested in biomedical applications of AI.

Research area: My area of research includes robotics, haptics, medical robotics, virtual fixtures, stability analysis, control systems, surgical robots, and smart surgical tools.

Student roles:
The student will design, develop, test and evaluate the system, including circuit design and microcontroller programming.

Skills required:
Electronic circuit design and development. Microcontroller and microprocessor programming.

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser or LED biostimulation to improve seed viability and resilience.

Student roles:
The student will contribute to the design, implementation, and analysis of seed biostimulation experiments using magnetic and electric field treatments. The student will begin by reviewing relevant literature on the effects of magnetic and electric fields on seed germination, seedling vigor, plant growth, and stress responses. This review will help guide the selection of treatment conditions, field strengths, exposure durations, and experimental protocols.

The student will assist with preparing seed samples, setting up magnetic and electric field treatment systems, applying controlled treatments, and maintaining consistent germination or growth conditions. The student will monitor germination over time and measure seedling growth traits such as root length, shoot length, biomass, germination rate, and seedling vigor.

Where applicable, the student will capture RGB images and use image processing tools to quantify seedling traits such as root and shoot development, seedling area, and growth rate. The student will organize experimental data, perform statistical analysis, prepare figures and tables, and summarize the results clearly.

The student will maintain detailed records of treatment settings, experimental procedures, environmental conditions, observations, datasets, and analysis workflows. They will prepare regular progress updates, contribute to the final project report, and may assist with preparing a conference abstract, manuscript draft, or future grant proposal. The student will work under supervision while also being encouraged to troubleshoot routine experimental issues and suggest improvements to the treatment protocols.

Skills required:
The student should have a background in agriculture, plant science, biosystems engineering, biological sciences, electrical engineering, physics, environmental science, or a related field. Basic knowledge of germination testing, experimental design, and data analysis would be beneficial. Experience with laboratory work, growth chambers, imaging, sensors, electric circuits, Python, MATLAB, or statistical software would be considered an asset.

The student should be willing to learn new experimental methods related to magnetic and electric field treatments, follow safety and laboratory protocols carefully, and maintain organized records.

Research area: Our research group operates within the Communications and Microelectronic Integration Laboratory (LaCIME) in the Department of Electrical Engineering at the École de technologie supérieure (ÉTS) and collaborates with members of the Regroupement Stratégique en microsystème du Québec (ReSMiQ). We develop organic semiconductor electronic devices including organic electrochemcial transistors, organic field effect transistors, printed chemiresistive and electrochemical sensors, printed RFID chipless antenna sensors, and soft MEMS such as ionic liquid crystal elastomers, for flexible applications in sensing, woundcare, and neuromorphic circuits. We focus on hands-on and modeling work, including cleanroom microfabrication (lithography, depositions), additive manufacturing processes, and material analysis.

Student roles:
The student will begin by reviewing the literature on printed electronics, CNT-based transistors, mm-wave circuits, and RFID backscatter systems. They will investigate the electrical and fabrication requirements needed to realize printed mm-wave switches and identify key challenges associated with these processes. Using simulation tools, the student will design and optimize switch structures and evaluate their performance at mm-wave frequencies. The project will also involve fabrication activities, including preparation of printed conductive patterns, characterization of printed materials, and development of fabrication procedures for active devices. Electrical measurements will be performed to assess switch performance, including insertion loss, isolation, switching behavior, and operating frequency limits. The student will maintain detailed documentation of fabrication procedures, simulation results, and measurement data, and will prepare technical reports and presentations summarizing project outcomes. Close collaboration with supervisors and researchers will be required throughout the project.

Skills required:
The ideal candidate should possess a background in electrical engineering, materials engineering, physics, or a related field. Knowledge of microwave engineering, semiconductor devices, and printed electronics is beneficial. Experience with simulation tools such as HFSS, CST, ADS, or Cadence would be valuable. Familiarity with fabrication techniques including inkjet printing, aerosol jet printing, screen printing, or microfabrication processes is considered an asset. Strong laboratory, analytical, teamwork, communication, and technical writing skills are essential.

Research area: Our research group operates within the Communications and Microelectronic Integration Laboratory (LaCIME) in the Department of Electrical Engineering at the École de technologie supérieure (ÉTS) and collaborates with members of the Regroupement Stratégique en microsystème du Québec (ReSMiQ). We develop organic semiconductor electronic devices including organic electrochemcial transistors, organic field effect transistors, printed chemiresistive and electrochemical sensors, printed RFID chipless antenna sensors, and soft MEMS such as ionic liquid crystal elastomers, for flexible applications in sensing, woundcare, and neuromorphic circuits. We focus on hands-on and modeling work, including cleanroom microfabrication (lithography, depositions), additive manufacturing processes, and material analysis.

Student roles:
The student will play a critical role in this multidisciplinary project, contributing to the design, simulation, and microfabrication of MEMS devices integrated with ionic Liquid Crystal Elastomer (iLCE) materials. Responsibilities include conducting comprehensive literature reviews, utilizing FEM simulation frameworks (such as COMSOL) for structural optimization, and executing hands-on cleanroom fabrication and electro-mechanical characterization. Additionally, the candidate will manage laboratory setups, analyze complex data sets, and document research outcomes for academic publication. This role requires strong communication, teamwork, and meticulous attention to detail. Through this immersive position, the student will gain advanced expertise in smart material integration and micro-engineering while directly contributing to next-generation, adaptive MEMS architectures.

Skills required:
The ideal candidate holds a background in MEMS, Electrical Engineering, or Materials Science, with core expertise in microfabrication, sensor/actuator design, and material characterization. Experience with smart materials—specifically the integration of ionic Liquid Crystal Elastomers (iLCEs)—is highly desired. Proficiency in FEM simulation frameworks (e.g., COMSOL Multiphysics or MATLAB) is essential for modeling device physics. Hands-on experimental skills and direct cleanroom experience are highly valued. Finally, the role requires a self-motivated researcher with robust analytical problem-solving capabilities, excellent communication skills, and a proven track record of documented research outcomes.

Research area: My specialized research area is in power electronics, smart energy systems, and advanced control of converter-based power systems. My work focuses on the modeling, control, and validation of grid-connected inverters, DC-DC converters, battery energy storage systems, and renewable energy interfaces. I am particularly interested in current control, voltage regulation, power quality improvement, weak-grid operation, and Hardware-in-the-Loop validation of control strategies for modern inverter-dominated energy systems. Using tools such as MATLAB/Simulink and real-time testing platforms, my research supports the development of reliable, efficient, and resilient control solutions for renewable energy and grid integration applications.

Student roles:
The student will assist with the modeling, control implementation, and Hardware-in-the-Loop validation of a grid-connected inverter system. Responsibilities will include developing simulation models, supporting controller design and tuning, setting up and running HIL test cases, analyzing performance under normal and disturbed operating conditions, and documenting results through plots, tables, and short technical summaries. The student will also participate in regular progress meetings and contribute to the final report and presentation.

Skills required:
The student will support the modeling, control implementation, and Hardware-in-the-Loop validation of a grid-connected inverter system. Their role will include developing simulation models in MATLAB/Simulink, assisting with controller design and tuning, setting up and running HIL test cases, analyzing current control and dynamic response results, and documenting findings through plots, tables, and short technical summaries. The student will also participate in regular progress meetings and contribute to the final report and presentation.

Research area: My specialized research area is in power electronics, control systems, and engineering education for smart energy technologies. My work focuses on the modeling, simulation, control, and experimental validation of power electronic converters, grid-connected systems, and renewable energy applications, with strong emphasis on MATLAB/Simulink-based analysis, Hardware-in-the-Loop testing, and hands-on laboratory development. I am also interested in designing practical and research-informed learning modules that strengthen student understanding of system dynamics, feedback control, PWM, and converter operation. This research and teaching focus supports the development of effective undergraduate laboratory experiences in control systems and power electronics.

Student roles:
The student will assist in developing, organizing, and validating undergraduate laboratory modules for control systems and power electronics. Responsibilities will include preparing step-by-step lab procedures, building simulation exercises in MATLAB/Simulink, supporting basic hardware or Hardware-in-the-Loop demonstrations where appropriate, testing experiments for clarity and reproducibility, and preparing supporting materials such as pre-lab tasks, data sheets, and result interpretation guides. The student will also document findings, participate in regular progress meetings, and contribute to the final lab manual and presentation.

Skills required:
The student should be an upper-year undergraduate in electrical engineering, mechatronics, engineering physics, or a related field, with basic knowledge of control systems, power electronics, electric circuits, and MATLAB/Simulink. Familiarity with PID control, converter operation, PWM, laboratory measurements, and hardware prototyping would be beneficial, but not mandatory. The student should also have good analytical, organizational, and technical writing skills, along with an interest in developing clear educational materials and validating laboratory experiments through simulation and basic hardware testing.

Research area: My specialized research area focuses on the design and characterization of RF (Radio Frequency) integrated circuits. In this field, I specialize in developing and optimizing circuits that operate at very high radio frequencies for various applications including communication, quantum computing and more.

Student roles:
The role of the student in the project would involve actively contributing to the various stages of the project, under the guidance of a project supervisor or research team. The student's responsibilities and tasks may include:
- Research and Literature Review
- PCB design
- Prototyping and Fabrication
- Characterization and Testing

Skills required:
A combination of technical skills and knowledge in the field of RF engineering and circuit design is needed. Some key skills needed for this project include:

- Basic understanding of analog and Mixed-Signal Design
- Basic understanding of PCB design
- Curiosity to learn RF circuits

Research area: My specialized research area focuses on the design and characterization of RF (Radio Frequency) integrated circuits. In this field, I specialize in developing and optimizing circuits that operate at very high radio frequencies for various applications including communication, quantum computing and more.

Student roles:
Depending on his/her experience, the intern will be involved in the design of the evaluation platform. The design will be carried out using commercial computer-aided design tools such as Altium Designer, Kicad, LT spice. The intern will be supported by graduate students and a postdoctoral researcher. The intern may also be involved in system-level laboratory testing, modeling and simulations.

Skills required:
The students should have taken basic courses in the field of analog electronics design. Knowledge of how to design, simulate electronics components is an asset.
Knowledge of integrated circuits design is an asset.
Knowledge of PCB design is an asset.

Research area: Our research group operates within the Communications and Microelectronic Integration Laboratory (LaCIME) in the Department of Electrical Engineering at the École de technologie supérieure (ÉTS) and collaborates with members of the Regroupement Stratégique en microsystème du Québec (ReSMiQ). We develop organic semiconductor electronic devices including organic electrochemcial transistors, organic field effect transistors, printed chemiresistive and electrochemical sensors, printed RFID chipless antenna sensors, and soft MEMS such as ionic liquid crystal elastomers, for flexible applications in sensing, woundcare, and neuromorphic circuits. We focus on hands-on and modeling work, including cleanroom microfabrication (lithography, depositions), additive manufacturing processes, and material analysis.

Student roles:
First, the student will need to complete required readings to understand iLCEs and different patterning methods. Then, they will proceed to generate various guiding structures with the goal of recreating results found in literature. To do so, they will fabricate the generated structures onto rigid substrates with lithography techniques. With those structures as templates, the student will finally create iLCE films. Their electrical performances will need to be tested onto provided OECT devices and their morphology will be investigated with polarized optical microscopy and SEM. Once those experimental control samples are fabricated and tested, the next phase of the project will be to generate guiding structures for specific OECT circuits. Past fabrication steps will need to be applied again to complete and test the new circuit destined iLCEs. These structures will need to preferably be generated programmatically with Python to be able to create ionic interconnects for various OECT devices and circuits.
Throughout the course of this project, the candidate will be expected to communicate findings through weekly presentations and thoroughly document their design process, fabrication process, test procedures and ideas. They will also need to seek guidance and training as needed.

Skills required:
An electrical or physics engineering background is essential. Clean room experience is a great asset. Proficiency in KLayout is beneficial, so is Python scripting as KLayout is compatible with this scripting language and would be very interesting for programmatically generating layout patterns.

Curiosity and creativity are important qualities that the candidate should demonstrate. The student should be able to prepare clear and detailed presentations and reports, as it is crucial to communicate findings and project progress to the rest of the team. Time management and teamwork skills are also key skills of an ideal candidate.

Research area: Our laboratory works on the design, optimization, and scale-up of photobioreactors for microalgae cultivation. We develop innovative cultivation systems that enhance mass transfer, light distribution, and hydrodynamic performance to maximize biomass productivity. Our work bridges fundamental fluid dynamics and applied biotechnology, targeting sustainable and cost-effective bioprocesses for environmental and industrial applications.

Student roles:
The intern will play a central role in the design, fabrication, and experimental testing of a flat-panel photobioreactor (FP-PBR) for microalgae cultivation. Working under the supervision of the principal investigator and in close collaboration with graduate students in the laboratory, the intern will contribute to all phases of reactor development.

In the design phase, the intern will review relevant scientific literature on flat-panel photobioreactor geometries, mixing systems, and gas injection strategies. They will assist in drafting design specifications and producing mechanical drawings or CAD models of the reactor components.

During fabrication, the intern will participate in the assembly and construction of the FP-PBR prototype using materials available in the laboratory or procured locally. This may include working with acrylic panels, gas spargers, pumps, and lighting systems, as well as setting up monitoring instrumentation (pH, dissolved oxygen, temperature, optical density).

In the experimental phase, the intern will conduct microalgae cultivation trials using CO₂, collecting data on biomass growth, CO₂ transfer efficiency, and reactor hydrodynamics. They will be responsible for maintaining culture conditions, performing routine analytical measurements, and systematically recording experimental results.

The intern will also contribute to data analysis, comparing reactor performance against established benchmarks and identifying design improvements. They are expected to present their findings in regular lab meetings and prepare a written technical report summarizing their work.

This internship offers hands-on training in bioprocess engineering, photobioreactor design, and microalgae biotechnology, providing the student with a strong foundation in both experimental research and sustainable bioprocess development.

Skills required:
- Electronics (e.g., Arduino, DAC/ADC)
- Coding (e.g., C++)
- Computer-Aided Design CAD (e.g., Solidworks)
- Motivation and autonomy

Research area: Are you curious about how fluids behave at incredibly small scales? Microfluidics is a fascinating field that focuses on controlling fluids at the sub-millimetre level — think droplets as wide as a single strand of hair! This area of research has exciting advantages, such as less reagent required (less expensive), shorter reaction time (more productive), and isolated individual reactions (cross-contamination mitigation). In this internship, you will get hands-on experience with smart active microfluidic systems that use real-time feedback and dynamic controls to manipulate fluids. Microfluidics has real-world applications in single-cell analysis, microparticle fabrication, and many more.

Student roles:
The student will lead this project with support from the team. The scope of the project will be modulated for the student. The responsibilities will include hardware and software design. Globally, the project will involve the design phase and review, prototype building and validation, and finally, the documentation for the publication of the open-source project.

Skills required:
- Electronics (e.g., Arduino, DAC/ADC)
- Coding (e.g., C++)
- Computer-Aided Design CAD (e.g., Solidworks)
- Motivation and autonomy

Research area: My specialized research area is in power electronics, smart energy systems, and advanced control of converter-based power systems. My work focuses on the modeling, control, and performance evaluation of grid-connected inverters, DC-DC converters, renewable energy systems, battery energy storage, and microgrids. I am particularly interested in current control, voltage regulation, power quality improvement, weak-grid operation, and intelligent control strategies for modern converter-dominated power systems. My research uses tools such as MATLAB/Simulink for system modeling, controller implementation, and validation to support the development of reliable, efficient, and resilient renewable energy technologies.

Student roles:
The student should be an upper-year undergraduate in electrical engineering, mechatronics, energy engineering, or a related field, with basic knowledge of power electronics, control systems, electric circuits, and renewable energy systems. Familiarity with MATLAB/Simulink is preferred, and exposure to grid-connected inverters, PWM, filters, or feedback control would be an asset. Experience with hardware prototyping, basic laboratory testing, and the use of measurement instruments would also be beneficial. The student should have good analytical and problem-solving skills, attention to detail, and the ability to document technical results clearly through plots, tables, and short summaries.

Skills required:
The student should be an upper-year undergraduate in electrical engineering, mechatronics, energy engineering, or a related field, with basic knowledge of power electronics, control systems, electric circuits, and renewable energy systems. Familiarity with MATLAB/Simulink is preferred, and exposure to grid-connected inverters, PWM, filters, or feedback control would be an asset. The student should also have good analytical and problem-solving skills, attention to detail, and the ability to document technical results clearly through plots, tables, and short summaries.

Research area: Industrial robots automate tasks for increased efficiency. Although rigid-bodied robots are well‑established in the field, soft robotics proposes a paradigm shift that promises to surpass the capabilities of current solutions. Soft robot manipulators enable flexibility through adaptability. Moreover, the common end effector applications exploit the softness and leniency of the robot manipulators to pick various fragile objects of different sizes, for instance, mushrooms, small berries or chocolatines. The handling of delicate objects is particularly important to the food and biomedical industries. The integration of soft robots also enables close collaboration with humans.

Student roles:
The student will lead this project with support from the team. The scope of the project will be modulated for the student. The responsibilities will include prototype fabrication and data acquisition software. Globally, the project will involve the design phase, prototype building, validation, and finally, documentation. Multiple iterations are expected to be completed within the timeframe of the internship.

Skills required:
- Control systems
- Electronics (e.g., Arduino)
- Coding (e.g., C++)
- Computer-Aided Design CAD (e.g., Solidworks)
- Motivation and autonomy

Research area: Our research group operates within the Communications and Microelectronic Integration Laboratory (LaCIME) in the Department of Electrical Engineering at the École de technologie supérieure (ÉTS) and collaborates with members of the Regroupement Stratégique en microsystème du Québec (ReSMiQ). We develop organic semiconductor electronic devices including organic electrochemcial transistors, organic field effect transistors, printed chemiresistive and electrochemical sensors, printed RFID chipless antenna sensors, and soft MEMS such as ionic liquid crystal elastomers, for flexible applications in sensing, woundcare, and neuromorphic circuits. We focus on hands-on and modeling work, including cleanroom microfabrication (lithography, depositions), additive manufacturing processes, and material analysis.

Student roles:
The students will be responsible for designing and analyzing the RFID antenna sensor, ensuring it meets the requirements for integration into a smart bandage system capable of monitoring its microenvironment. This involves utilizing engineering simulation software to optimize the sensor's performance. After this phase, they will gain expertise in fabrication techniques, employing printed methods to create simulated antennas in a clean room environment. Additionally, the student will learn how to test antenna performance by use of a vector network analyzer (VNA), assess its radiation properties in an anechoic chamber, and implementing necessary optimizations. Then they will assess the system’s operational efficiency in a real environment, when placed on skin in a bent state and by use of an RFID reader. Besides, regular documentation and reporting are crucial components of this role. The student will prepare detailed reports and presentations to document the design process, simulation results, fabrication methods, and testing outcomes. They will also be expected to communicate findings effectively to the team and seek guidance as needed.

Skills required:
The ideal candidate should possess strong teamwork and time management skills to effectively balance multiple tasks and meet project deadlines. Proficiency in preparing presentations and reports is crucial for communicating research findings effectively. Students must be enthusiastic about acquiring knowledge of new technologies. A background in engineering, particularly in antenna design and electromagnetics, is essential. Experience with simulation and design software such as Ansys HFSS or CST Microwave Studio is beneficial. Additionally, knowledge of printing technology, sensors, and antenna measurement tools would be a great asset.

Research area: My research focuses on computational design, structural timber engineering, and robotic fabrication for next-generation mass timber construction. I develop integrated CAD–CAE–CAM workflows that connect architectural geometry, structural analysis, fabrication constraints, and sustainability metrics within a unified digital framework. A central theme of my work is design automation for prefabricated and modular timber systems, including generative design tools, interlocking timber connections, digital fabrication strategies, and data-rich “element passport” models. My research aims to reduce design-to-fabrication fragmentation, improve material efficiency, support open-source engineering tools, and enable scalable, low-carbon timber construction through automation, structural performance, and manufacturing-aware design.

Student roles:
The intern will support the early-stage development of computational workflows for the design and fabrication of mass timber assemblies. Over the internship, the student will work under close supervision to translate research concepts into small, testable digital prototypes. The role will focus on implementing, documenting, and evaluating selected components of the broader tool suite, rather than developing a complete platform independently.

The student will begin by reviewing relevant examples of computational design and digital fabrication workflows for timber structures. This will include studying precedents in parametric modelling, automated joint generation, modular timber systems, and fabrication-aware design. Based on this review, the student will help identify specific workflow gaps that can be addressed through short-term tool development.

The student will then assist in developing prototype scripts or visual programming workflows, likely using Rhino, Grasshopper, and Python. These prototypes may include routines for generating timber components, creating joint geometries, organizing modular assemblies, assigning metadata to elements, checking basic geometric constraints, or preparing information for structural analysis and fabrication. Depending on progress, the student may also help test how design parameters influence geometry, material use, fabrication feasibility, or structural input generation.

A major part of the role will be careful documentation. The student will be expected to record assumptions, workflow steps, input and output parameters, limitations, and examples of use. This documentation will support future development by graduate students and researchers, and may contribute to open-source dissemination of the tools.

The student may also support visualization tasks, including diagrams, screenshots, workflow maps, and simple case-study models that communicate how the tools operate. By the end of the internship, the expected contribution is a clearly documented proof-of-concept workflow, accompanied by sample files, annotated code or Grasshopper definitions, and a short technical summary outlining what was developed, how it works, and how it

Skills required:
The student should have a strong interest in computational design, timber structures, digital fabrication, and sustainable construction. A background in civil engineering, architecture, structural engineering, computer science, or a related field is suitable. Experience with Rhino and Grasshopper is highly desirable, and basic programming ability in Python is preferred. Familiarity with structural analysis, parametric modelling, mass timber systems, or fabrication workflows would be an asset, but is not required. The student should be curious, organized, willing to learn new tools quickly, and comfortable working on an exploratory research project with both technical and design-oriented components.

Research area: In my research, I investigate how geometry influences the response of materials, robots, and structures. I exploit nonlinear phenomena found in folding origami motifs, cutting kirigami patterns, buckling elastic beams, and including defects in the topology of lattices. I develop theoretical, numerical, and experimental tools to tune and apply these phenomena in the fields of (1) multistable deployable structures; (2) instability-driven soft robots; and (3) 3D printed biomaterials.

Student roles:
•Proactively manage the research project under the supervision of professors.
•Participate in progress meetings and other activities essential to the proper operation of the laboratory.
•Draft technical reports, write refereed journal articles, attend international conferences, etc.
•Participate in clinical testing at the faculty of veterinary medicine in Saint-Hyacinthe.=

Skills required:
To conduct this research project, the ideal student should be creative, self-driven, familiar with computer aided design, programming, the finite elements method, prototyping via 3D printing and laser-cutting, and instrumentation.

Research area: In my lab, the Western Engineering for Spine and Trauma (WEST) Lab, we conduct orthopaedic engineering research to study the effects of load transfer through bones in the lower limb and spine. This includes evaluating fixation of implants to bone, implant/joint stability, and load transfer. One current major research theme is spine fusion. Clinically, fusion surgery is recommended to treat a number of spine conditions. We are interested in answering questions such as how does mechanical loading affects fusion in the spine? How can we design improved/novel/cheaper implants to promote better bone fusion?

Student roles:
The student will be based at our main office in Victoria Hospital, conducting interdisciplinary research to develop implants. They will enhance their technical engineering skills in numerical methods (e.g. FEM) and gain expertise in complex 3D modelling using CAD, 3D scannning, 3D printing and FEM modelling. Additionally, they will deepen their understanding of bone material properties and mechanical testing while applying these skills to clinically relevant problems.
Beyond developing their technical skills, the student will gain exposure to the patient care environment and improve their communication skills through collaboration with orthopedic surgeons and academic peers. The student will use the validated FEM bone model to simulate clinically relevant conditions of the spine and study the mechanical stability thereof. The student will also model common surgical implants used to stabilise the fusion site using computer aided design (CAD) software, to understand how the mechanical stability changes when these implants are introduced. Studies will be conducted using Abaqus FEM software.

Skills required:
Student must be enrolled in a relevant engineering field, e.g. mechanical, biomedical engineering degree with previous experience with or strong interest in biomechanics or CAD modelling applications to orthopaedic surgical problems.

Research area: Falls among older adults are a major concern in Canada and worldwide. Several approaches have been proposed to improve balance and reduce fall risk. One promising and accessible approach is reactive balance training, which aims to improve balance control by exposing individuals to perturbations large enough to challenge stability and potentially cause a fall. Controlled perturbations can be delivered using several methods, including mechanical robotic systems. Among these, cable-driven robotic systems are relatively low-cost, portable, and versatile.

Student roles:
The student will support the testing and evaluation of the cable-driven robotic system for balance training. Their role will include assisting with bench testing of the mechanical and control components, helping evaluate safety features, preparing and running experimental protocols, supporting data collection with healthy participants, and contributing to data processing and documentation. The student will work closely with the research team to ensure the system is tested safely, systematically, and in line with the project objectives.

Skills required:
Required Qualifications
• Academic background in engineering, computer science, kinesiology, biomechanics, or a related discipline
• Strong organizational skills and attention to detail
• Ability to work safely and professionally with human participants
• Good communication skills and ability to work as part of a research team
Preferred Qualifications
• Programming experience in MATLAB, Python, C++, or similar languages
• Familiarity with signal processing or data analysis
• Familiarity with mechanical systems, instrumentation, or mechanical testing
• Interest in rehabilitation robotics, biomechanics, or human movement research

Research area: Our research group specializes in tissue scaffold research. Tissue scaffolds are frameworks that can be used to regenerate damaged tissue that is difficult to treat by conventional methods. These scaffolds can be made by multiple manufacturing technologies to mimic the appearance and function of native tissue. Our research group focuses on 3D printing biomaterials to form hydrogel structures that can incorporate cells pre- or post-printing. Printed scaffolds need to be characterized by mechanical, architectural, and biological properties to assess their ability to mimic target tissues.

Student roles:
Over the course of the project, the Mitacs student will work directly with a graduate PhD researcher to create the complex articular cartilage tissue scaffolds.

The specific tasks that the student will engage in and learn will be:
(1) Fabricating complex scaffolds with natural and synthetic polymers. This will involve the use of 3D (bio)printing and melt electrowriting techniques.
(2) Assisting in the culturing and expansion of a chondrogenic cell line (ATDC5) to be incorporated in natural polymers to make a printable bioink.
(3) Assisting in bioink optimization for cell survival and simultaneously executing flow behaviour characterization with a rheometer.
(4) Carrying out characterization techniques to match the properties of scaffolds to the properties of native cartilage found in the literature. These will involve: (a) using bright field microscopy to observe the architectural properties; (b) using compression/tensile mechanical testing devices to assess the mechanical properties; (c) using biological staining and histology to assess the biological properties. There is also a small chance that the student will be able to experience synchrotron-based imaging at the Canadian Light Source, although this cannot be guaranteed due to scheduling.
(5) Analyzing and interpreting the data from the different characterizations to understand how different properties are affected by the designed parameters. This will involve preparing Excel spreadsheets and statistical software code to compare test data.

Skills required:
Highly motivated and adaptable students that have a background in mechanical/biomedical engineering or a background in biology/physiology with an interest in the overlap of health sciences and engineering are preferred. An understanding of engineering principles such as strain, stress, and shear would be an asset, and some experience with basic cell culturing and an understanding of biosafety would be useful. However, these concepts will be taught, trained, and reviewed over the course of the project. The student should have strong communication skills, the ability to work comfortably both individually and with a team, and strong critical thinking skills.

Research area: In my lab, the Western Engineering for Spine and Trauma (WEST) Lab, we conduct orthopaedic engineering research to study the effects of load transfer through bones in the lower limb and spine. This includes evaluating fixation of implants to bone, implant/joint stability, and load transfer. One current major research theme is broken bones. Clinically, broken bones are called "fractures". We are interested in answering questions such as how does bone loading affect healing following bone fractures in the lower limbs? How can we design improved/novel/cheaper implants to promote better bone bone healing?

Student roles:
The student will be based at our main office in Victoria Hospital, conducting interdisciplinary research to develop implants to improve bone fracture care. They will enhance their technical engineering skills in numerical methods (e.g. FEM) and gain expertise in complex 3D modelling using CAD, 3D scannning, 3D printing and FEM modelling. Additionally, they will deepen their understanding of bone material properties and mechanical testing while applying these skills to clinically relevant problems.
Beyond developing their technical skills, the student will gain exposure to the patient care environment and improve their communication skills through collaboration with orthopedic surgeons and academic peers. The student will use the validated FEM bone model to simulate bone fractures and study the mechanical stability of these fractures. The student will also model common surgical implants used to fix these fractures using computer aided design (CAD) software, to understand how the mechanical stability changes when these implants are introduced. Studies will be conducted using Abaqus FEM software.

Skills required:
Student must be enrolled in a relevant engineering field, e.g. mechanical, biomedical engineering degree with previous experience with or strong interest in biomechanics or CAD modelling applications to orthopaedic surgical problems.

Research area: My research focuses on developing low-carbon, energy-efficient, and climate-resilient building materials and envelope systems for sustainable construction. I specialize in bio-based materials, particularly hemp–lime composites, as well as thermal energy storage technologies, building durability, and hygrothermal performance. My work integrates experimental characterization, advanced modeling, optimization, and artificial intelligence to improve energy efficiency, indoor environmental quality, and carbon performance of buildings. Through collaborations with industry, government, and academic partners, I develop practical solutions that support building decarbonization, climate adaptation, and sustainable construction in diverse climatic conditions.

Student roles:
The student will play an active role in a multidisciplinary research project focused on developing and characterizing climate-resilient, bio-based building materials for low-carbon construction. Working under the supervision of the principal investigator and in collaboration with graduate students and other research team members, the student will contribute to both experimental and analytical aspects of the project.

Responsibilities may include preparing and testing material samples, conducting laboratory experiments, collecting and organizing experimental data, and assisting with the evaluation of physical, thermal, moisture-related, and durability properties of bio-based materials. Depending on the student’s background and interests, activities may involve measuring thermal conductivity, density, porosity, moisture performance, water absorption, drying behavior, and resistance to environmental degradation. Students may also assist with microscopy observations, image analysis, and the interpretation of experimental results.

In addition to laboratory activities, the student will participate in data processing, statistical analysis, literature reviews, and the preparation of technical summaries and research reports. Students with an interest in computational methods may also contribute to applying data analytics and artificial intelligence techniques to material performance prediction and optimization.

The student will attend regular research meetings, present progress updates, and participate in discussions with researchers, industry collaborators, and other stakeholders involved in sustainable construction research. Through these activities, the student will gain exposure to the complete research process, from experimental design and data collection to analysis, interpretation, and dissemination of knowledge.

The internship is designed to provide hands-on research experience while developing technical, analytical, communication, and teamwork skills. By the end of the project, the student will have contributed to advancing knowledge in sustainable building materials and gained valuable experience working within an internationally collaborative research environment focused on addressing challenges related to building decarbonization, energy efficiency, and climate resilience.

Skills required:
Applicants should have a strong academic background in civil engineering, building engineering, materials engineering, environmental engineering, mechanical engineering, architecture, or a related field. An interest in sustainable construction, building materials, energy efficiency, or climate resilience is desirable. Experience with laboratory work, data analysis, statistics, programming, or machine learning is considered an asset but is not required. Successful candidates should demonstrate strong analytical and problem-solving skills, attention to detail, initiative, and the ability to work both independently and as part of a multidisciplinary research team. Excellent written and verbal communication skills are desirable.

Research area: In general, my research aims at increasing the sustainability and intelligence of the design, construction, and maintenance of infrastructure and buildings. It consists of three major components: 1) Application of AI technologies for design automation. 2) Application of computer vision and robotics to improve the safety and productivity of construction operations. 3) Infrastructure and building condition assessment using the Internet of Things with public participation (Crowdsensing-based infrastructure and building health monitoring).

Student roles:
1. Write computer programs to operate robots.
2. Extract data from sensors mounted on the mobile robot.
3. Write documents and reports in a professional fashion.
4. Communicate and collaborate with other researchers in the group effectively.
5. Work with other tasks assigned by the supervisor.

Skills required:
1. Knowledge of computer programming is a must.
2. Highly motivated, well organized, and passionate about research with good communication skills in English.
3. Self-starter and motivator with the mindset of focusing on deliverables and getting the job done on time.
4. Experience in report writing and publishing is an asset.
5. Experience in robotics is an asset.
5. Experience in machine learning is an asset.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 20 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with project tasks
training will be provided.

Skills required:
Mechanical/production engineering /Mechatronics
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser or LED biostimulation to improve seed viability and resilience.

Student roles:
The student will contribute to the design, development, and preliminary evaluation of a low-cost infrared spectrometer for agri-food quality assessment. The student will begin by reviewing existing low-cost spectroscopy systems, infrared sensing technologies, optical components, and relevant agri-food applications. This review will guide the selection of suitable components and the overall prototype design.

The student will assist with selecting and integrating optical and electronic components, such as infrared light sources, filters or dispersive elements, detectors, lenses, microcontrollers, and data acquisition modules. They will help assemble the prototype, develop a sample presentation setup, and implement basic data acquisition procedures.

The student will collect spectral measurements from selected agricultural or food samples and develop signal processing workflows for dark correction, reference correction, noise reduction, normalization, and visualization. They may also develop preliminary calibration or classification models using Python, MATLAB, or similar tools, depending on the availability of reference data.

The student will evaluate the prototype’s performance by examining signal repeatability, sensitivity to sample differences, ease of use, cost, and practical limitations. Where possible, results may be compared with commercial spectroscopy systems or standard laboratory measurements.

The student will maintain detailed records of component selection, prototype design, experimental procedures, datasets, analysis codes, and results. They will prepare progress updates, contribute to the final project report, and may assist with preparing a conference abstract, manuscript draft, or future grant proposal. The student will work under supervision while also being encouraged to troubleshoot technical challenges and suggest design improvements.

Skills required:
The student should have a background in electrical engineering, computer engineering, biosystems engineering, mechanical engineering, physics, computer science, data science, agriculture, food science, or a related field. Basic programming skills in Python, MATLAB, or a similar platform are required. Experience or interest in optics, sensors, electronics, spectroscopy, embedded systems, data acquisition, signal processing, machine learning, or agri-food quality assessment would be highly beneficial.

The student should be willing to work with both hardware and software tools, follow laboratory procedures, organize experimental data, and communicate results clearly.

Research area: In general, my research aims at increasing the sustainability and intelligence of the design, construction, and maintenance of infrastructure and buildings. It consists of three major components: 1) Application of AI technologies for design automation. 2) Application of computer vision and robotics to improve the safety and productivity of construction operations. 3) Infrastructure and building condition assessment using the Internet of Things with public participation (Crowdsensing-based infrastructure and building health monitoring).

Student roles:
1. Write computer programs to operate robots.
2. Extract data from sensors mounted on the mobile robot.
3. Write documents and reports in a professional fashion.
4. Communicate and collaborate with other researchers in the group effectively.
5. Work with other tasks assigned by the supervisor.

Skills required:
1. Knowledge of computer programming is a must.
2. Highly motivated, well organized, and passionate about research with good communication skills in English.
3. Self-starter and motivator with the mindset of focusing on deliverables and getting the job done on time.
4. Experience in report writing and publishing is an asset.
5. Experience in robotics is an asset.
6. Experience in VR/AR is an asset.
7. Experience in vibe coding is an asset.

Research area: My lab brings together a diverse group of inter-disciplinary researchers interested in collaborative research to improve sensory motor control and integration of advanced prosthetic and robotic systems. The lab encompasses research projects advancing prosthetic and robotic care. We are intensely interested in the measurement of human systems behaviour that allows us to investigate the impacts of technological interventions on clinical outcomes. Our unique combination of medical, rehabilitation, engineering and computing science researchers has allowed the evolution of multiple lines of complementary research aimed at improving the science and art of prosthetic restoration and rehabilitation robotics.

Student roles:
- Programming of prosthetic device models in virtual reality Unity Game Engine
- Development of an information pipeline from multiple costume devices into a Virtual reality platform
- Integration of advanced novel machine learning control algorithms signals implemented in our custom software with our virtual reality Platform
- Create online documentation of developed systems and software

Skills required:
- Knowledge of programming languages such as C, C++, and C# in visual studio or equivalent.
- Basic understanding of virtual environment technology.
- Use of online code version control repositories such as github.
- Knowledge of programming in Unity game engine is an asset, but not required.

Research area: My research focusses on the development and clinical evaluation of rehabilitation robots and electrical stimulation interfaces for upper limb stroke rehabilitation. We have developed the HERO Glove hand exoskeleton and work with a team of researchers, engineers, therapists and patients to refine the design based on clinical needs.

Student roles:
The student will design custom circuit boards that measure electromyography signals and evaluate how well the system can measure small changes in signal produced by stroke patients. The student will have the opportunity to develop skills in mechatronics, control systems and game development to create a system that can be deployed in rehabilitation programs.

Skills required:
Students should have experience designing electric circuit boards.
Students should have a passion of biomedical engineering and designing systems for clinical populations.

Research area: My research focuses on the development of complex products and systems in the context of business and societal transformation. My research focuses on the product development structure, and especially its hierarchical organization of approaches, processes, methods and tools needed to support the development of complex products within companies and start-ups. In this respect, model-based systems engineering is one of my main research areas, which transfers into my teaching. In terms of field of application, aeronautics is particularly considered in this project, in line with the development of a course.

Student roles:
Students should be able to produce educational material for an MBSE course in aeronautics. This material will take the form of systems examples, various models for documenting system development, including requirements engineering, system architecture, module design, behavioral modeling, and possibly 3D parts.

The 12 weeks can be broken down as follows:

-Getting acquainted with the concepts of systems thinking, systems engineering and MBSE (1 to 3 weeks). This includes reading various articles, standards, books and videos related to these concepts.
-Getting started with Catia Magic and SysML (1 week).
-Identify examples of systems and their contexts that can be modeled. (1 week).
-For one of the systems identified, propose a requirements structure (1 week).
-Refine the requirements into a system architecture proposal, together with its modules and interfaces (1 to 2 weeks).
-Propose a simplified 0D/1D simulation model of the system (system behavioral modeling) (1 to 2 weeks).
-If time allows, simulate the impact of a change in requirements (1 week).
-In parallel, and as modeling progress, structure information and propose practical guides (3 weeks). Video recordings could be made on the software to complement the guides.
-In the final weeks, write a project report (1 to 2 weeks). The main purpose of this stage is to document the work carried out.

Note that this is an ongoing project and few adjustments could be made, an updated plan can be discussed at the time of the interview.

Skills required:
Students with a strong interest in the design and development of complex systems. A background in systems engineering and model-based systems engineering (MBSE) is desirable, but not mandatory. Previous experience in systems development is also preferred. Knowledge of SysML (v1 or v2) and/or MBSE tools on TeamCenter is an asset.

Students are expected to be autonomous (weekly meetings), to be able to do readings on their initiative, and to have excellent synthesis skills.

Research area: Mina Hoorfar is a Professor and Dean at the Faculty of Engineering and Computer Science at the University of Victoria where she leads the Microfluidics and Nanotechnology Laboratory (MiNa Lab). She is known nationally and internationally for her research and innovation in the field of microfluidics, combining knowledge from areas such as fluid mechanics and biochemistry, as well as those related to the fabrication of biosensors and gas sensors. Her work has been applied in a variety of applications, such as water and air quality monitoring, in collaboration with industry partners in oil and gas, municipalities, and life sciences sectors.

Student roles:
The student will participate in all aspects of research including literature review, prototype design and assembly, sample preparation, experiment planning and completion, and data analysis. The student will be required to complete all safety training and certifications required to conduct laboratory experiments and other research work. Roughly, the first 2-3 weeks will be focused on lab orientation, literature review, and project planning. The next 5-6 weeks will be devoted to carrying out the defined project, including prototype assembly and testing. The final 3-5 weeks will involve more refined experiments and data collection, prototype modifications if needed, and compiling the data in terms of a report, publication, or presentation depending on the results. The student will work closely with a working group comprised of research lab managers and research assistants to define a project plan and timeline. Weekly meetings will be held to track the progress of the project where the student will be required to present their ongoing work, any challenges encountered, and next steps. The student will also be exposed to other ongoing research activities at our laboratory and can shadow graduate students and/or research staff.

Skills required:
The Microfluidics and Nanotechnology (MiNa) lab is interested in hosting a senior Mechanical, Electrical, Electronics, or similarly relevant Engineering student with knowledge and hands-on experience with 3D design, circuit assembly, and coding. Knowledge or interest in spectroscopy techniques is appreciated for this role. The student must be comfortable conducting literature review, executing project tasks independently, and working within a project team. Technical writing skills are desired, and the applicant must be enthusiastic about disseminating their research findings.

Research area: My research activities include : (i) binder technology for road & construction materials, (ii) carbon capture studies for binders, (iii) adsorption studies of novel regenerative hybrid magnetic SBA 15, (iv) characterization and remediation techniques of problematic soils, (v) effects of temperature and pore fluid salinity on clay barriers, (vi) mechanical behaviour of clay barriers at different suction levels and (vii) smart design of permeable reactive barriers. I have been working on experimental techniques and therefore, have established a successful hands-on HQP training program in my lab.

Student roles:
The research laboratory has advanced testing facilities which require special training to operate. The student will be trained to use the equipment and provided with the necessary background knowledge to carry out research according to protocols laid out by primitive researchers. The student will work with a team of scholars to share resources, exchange ideas and scientific concepts, as well as assisting other students when it is required. Time management skills are essential as this position involves determined milestones and several interactions with the industrial partners. The student will work within clear deadlines for completing deliverables. The student will also work on collecting experimental data, statistical analyses of data sets, and computer programs related to the research field. The objective of the project is clearly defined for the student; however, the student will have opportunities to develop protocols that will work best for carrying out the design process. This will encourage the student to become involved in a teamwork setting and find synergy among their skills and capabilities.

Skills required:
The intern needs to have a great perseverance and enthusiasm for the sake of the research project. Besides, the student needs to have high levels of communication and time management expertise and the ability to work independently and within a team. The student should also have general problem solving and multitasking skills as well as a motivation for learning new technologies to have a satisfactory progress in their assigned project along with courses. Furthermore, the student is required to attend weekly research group meetings and to protect confidential research outcomes of the project.

Research area: My work is in systems and control theory, signal processing, security and privacy mechanisms for cyber-physical systems and intelligent infrastructures, and autonomous mobile robotics. In the context of this project, my group's relevant work is in integrating privacy and security-preserving mechanisms into sensor and actuator networks.

Student roles:
The student will need to read the basic literature on differential privacy and graph signal processing. They will then design new algorithms for differentially private graph signal filtering.

Skills required:
- The student should have a taste for theory in at least one of the relevant areas: systems and control theory, signal processing, theoretical computer science, statistics, etc.
- Ability to design and implement algorithms in a high-level language (Matlab, Python, Julia, etc.).
- Mathematical maturity.

Research area: My research mainly focuses on critical areas related to electric power systems. The three main areas of my research are: i) modeling, ii) protection and iii) control of HVDC systems and VSC-interfaced renewable energy resources. The overall objective of my research is to investigate and overcome the challenges associated with the realization of mixed AC-DC power systems. I am also interested in real-time simulation and hardware-in-the-loop (HIL) testing of various protection and control equipment.

Student roles:
This project focuses on setting up a digital protection system. A physical-digital protection system will be set up and connected to a real-time digital simulator where a distribution system will be simulated. Then, the parameters of the digital relay will be tuned to provide reliable protection for the distribution system.

Skills required:
The student must determine the various components necessary for developing a digital protection system in a modern power system with multiple RESs. The specifications of all the components including merging units, IEDs, communication network and CTs and VTs should be identified.

Research area: Dr. Aitazaz A. Farooque specializes in precision agriculture, climate-smart agricultural technologies, remote sensing, GIS, artificial intelligence, agricultural automation, and environmental sustainability. His research focuses on developing advanced geospatial and sensing technologies for sustainable agriculture, crop monitoring, water management, climate adaptation, and digital agriculture applications. His work integrates AI, machine learning, computer vision, GPS-GIS systems, and precision farming technologies to improve agricultural productivity and environmental management. Dr. Farooque leads several nationally and internationally recognized research initiatives at the University of Prince Edward Island and the Canadian Centre for Climate Change and Adaptation. More information: https://theatlaslab.ca/

Student roles:
The student will contribute to the design and development of a digital twin and AI decision-support system for climate change risk and resilience planning. The student will collect, organize, and process datasets related to climate variables, historical weather events, environmental conditions, land cover, elevation, infrastructure exposure, and vulnerable areas. They will support the development of structured databases and data pipelines that can bring different sources of climate-related information into one integrated platform.

The student will also assist in developing and evaluating AI and machine learning models for climate risk identification, impact forecasting, and scenario analysis. This may include modelling flood-prone areas, coastal vulnerability, heat-risk zones, or other climate-related hazards depending on available datasets and project priorities. The student will help validate model outputs, prepare visualizations, and translate complex climate data into clear decision-support information.

In addition, the student will support the development of an interactive dashboard or web-based platform where users can view risk maps, compare climate scenarios, explore vulnerability indicators, and generate reports. The role will involve data processing, model testing, platform development, documentation, and preparation of technical reports or research outputs. This project is suitable for a student interested in AI, digital twins, climate change adaptation, environmental analytics, and technology-driven resilience planning.

Skills required:
The ideal student should have a background in computer science, data science, environmental science, climate science, engineering, GIS, remote sensing, or a related discipline. Experience with Python, machine learning, climate data analysis, databases, APIs, web development, dashboard design, and data visualization would be valuable. Familiarity with climate change impacts, flood risk, coastal vulnerability, environmental modelling, and decision-support systems would be an asset. The student should have strong analytical, programming, problem-solving, and communication skills, with interest in climate resilience, AI applications, and digital platform development.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 20 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with project tasks
training will be provided.

Skills required:
Mechanical/production engineering /Mechatronics
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: Digital twin, smart cities, smart grid, transportation systems, communication systems, machine learning, large language models

Student roles:
This project will host two students who collaborate closely throughout the 12-week internship, working with the supervisor and graduate students.
Student A - Subsystem Modeling & Analysis: This student will build two smart city subsystem modules by integrating existing mathematical models, simulators, and data-driven methods. They will define the common interface variables exchanged between subsystems and conduct the core empirical study: measuring how varying levels of disclosed context information and data uncertainty affect cross-system coordination performance.
Student B - LLM Integration & Orchestration: This student will develop the LLM-based intermediate layer that automatically generates adapter code between the subsystem modules and aligns their cross-system variables. They will integrate this layer with Student A's coupled modules and demonstrate improved coordinated operation relative to a fixed, hand-specified baseline interface.

Skills required:
Students are required to have strong programming ability in Python and comfort with relevant libraries such as NumPy, pandas, PyTorch and more. Students should have foundational knowledge of machine learning and data analysis. Familiarity with simulation platforms such as OpenDSS or network simulators is a strong asset. Students should also have some exposure to LLMs and API usage. Students should have strong analytical thinking, self-motivation, and strong English writing and speaking skills.

Research area: We develop mathematical models and computational libraries to study the mechanics of active matter. Active matter is a novel and exciting class of materials that include suspensions of motile bacteria, molecular motors, and synthetic self-propelled colloids. Potential applications of active matter include catalysis, chemical sensing, self-assembling intelligent materials, and targeted drug delivery. Compared to passive materials, active matter is inherently out of thermodynamic equilibrium. As a result, their behavior is often counterintuitive and the understanding of the underlying physics of such materials remains lacking. In our work, we aim to predict the behavior of active matter under various conditions.

Student roles:
Role 1: Developing mathematical models for the transport of magnetic active particles in nonlinear flows. Starting from the advection-diffusion equation that governs the behavior of an active particle in a prescribed flow field, we develop a reduced model that describes the long-time dynamics of the particle. The reduced model comes in the form of a set of differential equations, and the transport coefficients such as the mean speed and effective diffusivity can be obtained by integration of the solution against a known function of space. We will solve the resulting reduced equations using existing software packages. The student will validate the solution, produce visualizations, and plots. The results obtained will become part of an academic journal paper and the student will be a co-author.

Role 2: Computer simulation of magnetic active particles in nonlinear flows. The student will learn to use an existing software package (in Python) developed in our group to perform particle-based Brownian dynamics simulations of active particles. In essence, this means that we are integrating the Newtonian equations of motion of an active particle. With this, we produce the trajectory of active particles in space as a function of time. From these trajectories, we can calculate the mean speed and effective diffusivity by looking at the mean and mean-squared displacements as a function of time. By linear fitting, we can extract the slope of these curves and obtain the transport coefficients. The results obtained will become part of an academic journal paper and the student will be a co-author.

Role 3: Computer simulation of magnetic active particles in complex environment. The student will learn to use an existing software package (in Python) developed in our group to perform particle-based Brownian dynamics simulations of active particles.

Skills required:
For the modeling part, the ideal candidate would have skills in basic fluid mechanics and applied mathematics, including differential equations and finite differences. Computer programming in Matlab or Python, and data visualization in Matlab/Python are also useful.

For the simulation part, computer programming in Matlab or Python or Julia, and data analysis and data visualization are essential. A basic understanding of fluid mechanics is beneficial.

Research area: My research field focuses on aerodynamics and fluid mechanics applied to ventilation systems. The objective is to understand turbulence phenomena and boundary layer separation at the blade scale to improve energy efficiency. We use standardized airflow test benches to measure overall performance (pressure, flow rate, power) while integrating advanced measurement techniques. The challenge is to reconcile high air transfer performance with a drastic reduction in noise pollution, thereby meeting current environmental standards.

Student roles:
The student will be the operational pivot of the project. Their role is multidimensional and covers the entire research chain:

Design and Assembly: The student will be responsible for finalizing the test bench. This includes selecting materials for the settling chamber, installing honeycombs to straighten the flow, and mounting the drive motor with its variable frequency drive (VFD).

Measurement Protocol: They will need to define and rigorously apply testing protocols. This involves calibrating sensors, verifying measurement repeatability, and managing experimental uncertainties. They must ensure that each test produces usable and scientifically valid data.

Prototyping and Iteration: Using 3D printing, the student will manufacture different fan variants (blade pitch angle, camber, number of blades). They will test each iteration and analyze the impact of geometric modifications on overall efficiency.

Signal Processing and Reporting: A significant part of the work will consist of processing raw data via Python or MATLAB to extract pressure and power coefficients. The student will synthesize their findings in the form of technical reports and presentations, highlighting the trade-offs between airflow and power consumption.

Scientific Collaboration: They will work closely with numerical simulation specialists to compare the test bench results with theoretical predictions, thereby playing a "validator" role for CFD models.

In short, the student will act as a test and design engineer, combining manual rigor with critical analysis skills to advance the understanding of the system's aerodynamic behavior.

Skills required:
The student must possess a solid foundation in fluid mechanics (Euler theory, Navier-Stokes equations) and thermodynamics. Proficiency in Computer-Aided Design (CAD) tools, such as SolidWorks or CATIA, is essential for modifying prototypes. Instrumentation skills (LabVIEW, Arduino, or Python) are required to automate the data acquisition chain. Finally, a curiosity for practical experimentation is essential: the candidate must be comfortable with mechanical assembly, sensor wiring, and the metrological rigor specific to laboratory testing.

Research area: L'Université de Sherbrooke, le groupe d'innovation Createk et l'entreprise Dana TM4 ont lancé, en 2026, la Chaire de recherche Dana TM4 pour le développement de technologies de rupture en propulsion électrique. Ce projet d'envergure vise à développer la prochaine génération de systèmes de propulsion électrique pour le transport automobile. La personne étudiante évoluera au sein du groupe d’innovation Createk. Ce groupe a pour mission de supporter l'innovation en favorisant les liens entre la recherche et l'industrie et entretien une communauté de « makers » avec un accès à une large panoplie d'équipement de prototypage dans son atelier "FabLab".

Student roles:
La personne candidate sera appelé à supporter le développement d'un onduleur SiC présentement en cours dans le cadre d'un projet de propulsion électrique. Plusieurs tâches connexes sont associées au au développement de l'onduleur comme sa simulation, son optimisation, la conception de son système de refroidissement, la conception de son circuit imprimé, la conception de son boitier de protection, sa fabrication ainsi que sa validation expérimentale en laboratoire. Le rôle de la personne stagiaire consiste à supporter l'équipe dans l'accomplissement de ces mandats. Tout dépendamment l'intérêt de la personne candidate, elle pourra s'initier à un où plusieurs aspects de développement de l'onduleur. Le stagiaire est aussi mandaté de documenter son travail dans des courts rapports d'avancement, de résumer les résultats obtenus dans des présentations de suivi pour le partenaire industriel et à participer activement au travail d'équipe au sein du projet. L'équipe se rencontre une fois par semaine pour planifier les tâches puis rencontre le partenaire à chaque mois pour présenter les avancements. Il s'agit aussi d'une belle opportunité pour entrer en contact avec des experts du domaine évoluant en industrie qui procurent des conseils judicieux autant pour la conception d'onduleurs que de moteurs électriques.

Skills required:
Qualités et expérience recherchées :

- Motivation et curiosité
- Autonomie
- Travail en équipe et bonne communication
- Formation en cours dans le domaine du génie électrique
- Intérêt pour le domaine de l'automobile et de l'électronique de puissance
- Expérience ou intérêt envers les logiciels : Altium, KiCad, ANSYS electronics...

Research area: My research lies at the intersection of mechatronics engineering, artificial intelligence, and cardiovascular physiology. I develop hybrid solutions that combine physiological models and data-driven approaches to improve the monitoring of vital signs, predict the need for medical intervention, and optimize devices related to the cardiovascular system. Keywords: Arterial stiffness, Biosignals, Cardiovascular physiology, Control, Dynamic modeling, Machine learning, Mechatronics, Signal processing, System identification, Viscoelasticity

Student roles:
The intern will play an active role in the development of a prototype medical-grade cuffless blood pressure monitor. Depending on their profile, they will contribute to the mechatronic design of the device, the modeling of arterial mechanics, the processing of physiological biosignals, or the development and validation of machine learning models.
In collaboration with researchers in engineering and health sciences, they will take part in the collection and analysis of experimental data obtained from measurements on human subjects.
This internship offers a unique opportunity to contribute to an interdisciplinary project with strong clinical impact, within a stimulating and collaborative research environment.

Skills required:
Must have at least one of the following skills:
- Mechatronic device design
- Mechanical design
- Electronics design
- Signal processing
- Mechanical modeling
- Data-driven modeling
- Machine learning

Research area: L'Université de Sherbrooke, le groupe d'innovation Createk et l'entreprise Dana TM4 ont lancé, en 2026, la Chaire de recherche Dana TM4 pour le développement de technologies de rupture en propulsion électrique. Ce projet d'envergure vise à développer la prochaine génération de système de propulsion électrique pour le transport automobile. La personne étudiante évoluera au sein du groupe d’innovation Createk. Ce groupe a pour mission de supporter l'innovation en favorisant les liens entre la recherche et l'industrie et entretien une communauté de « makers » avec un accès à une large panoplie d'équipement de prototypage dans son atelier "FabLab".

Student roles:
La personne candidate sera appelé à supporter le développement mécanique et les essais expérimentaux d'une machine électrique ultra-haute vitesse sans terres rares présentement en cours dans le cadre d'un projet de propulsion électrique. Plusieurs tâches connexes sont associées au au développement de la machine comme sa simulation, sa modélisation 3D, la conception de son système de refroidissement, l'idéation de concepts pour ses sous-systèmes, la conception du système de roulements, sa fabrication ainsi que sa validation expérimentale en laboratoire. Le rôle de la personne stagiaire consiste à supporter l'équipe dans l'accomplissement de ces mandats. Tout dépendamment l'intérêt de la personne candidate, elle pourra s'initier à un où plusieurs aspects de développement de la machine. Le stagiaire est aussi mandaté de documenter son travail dans des courts rapports d'avancement, de résumer les résultats obtenus dans des présentations de suivi pour le partenaire industriel et à participer activement au travail d'équipe au sein du projet. L'équipe se rencontre une fois par semaine pour planifier les tâches puis rencontre le partenaire à chaque mois pour présenter les avancements. Il s'agit aussi d'une belle opportunité pour entrer en contact avec des experts du domaine évoluant en industrie qui procurent des conseils judicieux autant pour la conception d'onduleurs que de moteurs électriques.

Skills required:
Qualités et expérience recherchées :

- Motivation et curiosité
- Autonomie
- Travail en équipe et bonne communication
- Formation en cours dans le domaine du génie électrique
- Intérêt pour le domaine de l'automobile, l'électronique de puissance et des machines électriques
- Expérience ou intérêt envers les logiciels : MotorCAD, Solidworks, ANSYS mechanical, Python, Matlab...

Research area: L'Université de Sherbrooke, le groupe d'innovation Createk et l'entreprise Dana TM4 ont lancé, en 2026, la Chaire de recherche Dana TM4 pour le développement de technologies de rupture en propulsion électrique. Ce projet d'envergure vise à développer la prochaine génération de systèmes de propulsion électrique pour le transport automobile. La personne étudiante évoluera au sein du groupe d’innovation Createk. Ce groupe a pour mission de supporter l'innovation en favorisant les liens entre la recherche et l'industrie et entretien une communauté de « makers » avec un accès à une large panoplie d'équipement de prototypage dans son atelier "FabLab".

Student roles:
La personne candidate sera appelée à supporter le développement électromagnétique d'une machine électrique ultra-haute vitesse sans terres rares présentement en cours dans le cadre d'un projet de propulsion électrique. Plusieurs tâches connexes sont associées au au développement de la machine comme sa simulation, son optimisation, la conception de son système de refroidissement, l'idéation de concepts pour ses sous-systèmes, la conception de son boitier, sa fabrication ainsi que sa validation expérimentale en laboratoire. Le rôle de la personne stagiaire consiste à supporter l'équipe dans l'accomplissement de ces mandats. Tout dépendamment l'intérêt de la personne candidate, elle pourra s'initier à un où plusieurs aspects de développement de la machine. Le stagiaire est aussi mandaté de documenter son travail dans des courts rapports d'avancement, de résumer les résultats obtenus dans des présentations de suivi pour le partenaire industriel et à participer activement au travail d'équipe au sein du projet. L'équipe se rencontre une fois par semaine pour planifier les tâches puis rencontre le partenaire à chaque mois pour présenter les avancements. Il s'agit aussi d'une belle opportunité pour entrer en contact avec des experts du domaine évoluant en industrie qui procurent des conseils judicieux autant pour la conception d'onduleurs que de moteurs électriques.

Skills required:
Qualités et expérience recherchées :

- Motivation et curiosité
- Autonomie
- Travail en équipe et bonne communication
- Formation en cours dans le domaine du génie électrique
- Intérêt pour le domaine de l'automobile, l'électronique de puissance et des machines électriques
- Expérience ou intérêt envers les logiciels : MotorCAD, Solidworks, ANSYS electronics, Python, Matlab...

Research area: My research focuses on Artificial Intelligence, Machine Learning, and Biomedical Signal Processing. I develop data-driven AI solutions that integrate multimodal sensing, temporal modeling, and physiological signal analysis for prediction, classification, and decision-making tasks in complex human and engineered systems. My work spans multimodal learning, multitask learning, affective computing, signal processing, and representation learning, with applications in healthcare, neuroengineering, and intelligent sensing technologies. Keywords: Artificial Intelligence, Machine Learning, Multimodal Learning, Biosignals, Physiological Signal Analysis, Affective Computing, Neuroengineering, Signal Processing, Human-Machine Interaction, Intelligent Sensing

Student roles:
The student will contribute to the development and evaluation of artificial intelligence models for physiological signal analysis and neural-state estimation. Responsibilities will include signal preprocessing, feature extraction, implementation of machine learning and deep learning algorithms, and performance evaluation using EMG and EEG data. The student will investigate relationships between muscle and brain activity and develop models for real-time estimation of neural states. In a second phase, the student will contribute to the design and evaluation of closed-loop control strategies that adapt neurostimulation parameters based on estimated brain states. The student will also assist with data collection, result interpretation, technical reports, and scientific publications.

Skills required:
Must have at least one of the following skills:
1. Machine Learning / Deep Learning
2. Signal Processing
3. Control Systems
4. Data-driven Modeling
5. Time-Series Analysis

Research area: My research interests include microelectronic circuits for energy harvesting applied to thermoelectric sensors and radio waves. I am also interested in renewable energies, primarily the design of photovoltaic solar energy production systems.

Student roles:
The student will need to be able to model the optical behavior of lenses and reflectors and master the fundamental concepts of the optical characteristics of materials, such as reflection, absorption, and transmission. Furthermore, they will need to be able to differentiate between thermal and electrical conductors and understand their insulating properties. Therefore, the student will be assigned a design engineering project.

Skills required:
This project falls under the umbrella of multidisciplinary engineering: electrical, physical, and materials science. However, for this initial development phase, a solid foundation in optical physics and materials science is sufficient. Proficiency in using computer tools would be an asset: Python, optical modeling software (Ansys Zemax), and finite element modeling software. The project can be adapted to the student's skill level.

Research area: Your area: At IDEA Lab (goidealab.com) at the University of Alberta, we are focused on developing autonomous intelligent systems to deliver personalized health, specially using wearable technologies and robotic systems. With accessible wearable technologies such as consumer-grade smartwatches, we can measure movement and physiological data. With cost-effective robotic systems, we can perform assessment of human function and deliver therapy. Commonly, we combine these with artificial intelligence (e.g., deep learning) and biomedical signal processing. In this project, we aim to use AI to monitor physiological signals (e.g., heart rate) and facial gestures to quantify user engagement when working with a robot.

Student roles:
Your role in the "EmoSense" project will be integral to its success. You will have the opportunity to actively contribute to various aspects of the project, gaining hands-on experience in research, data analysis, and artificial intelligence techniques. Here is an overview of the key responsibilities and tasks associated with your role:

- Data Collection and Preparation: You will assist in the setup and calibration of wearable GSR and PPG sensors, ensuring accurate data collection during the experimental sessions. This involves knowledge of sensor placement, data synchronization, and troubleshooting any technical issues that may arise. Additionally, you will be responsible for organizing and preprocessing the collected data, ensuring its quality and suitability for analysis.

- Algorithm Development and Implementation: You will contribute to the development of algorithms for feature extraction, classification, and regression. This will involve leveraging machine learning and deep learning techniques to analyze the GSR, PPG, and facial gesture data. You will be responsible for implementing these algorithms using programming languages like Python or MATLAB and relevant libraries.

- Experimental Design and Data Analysis: Collaborating with the project team, you will contribute to the design of experiments aimed at studying user engagement in robotic and virtual reality environments. This includes designing protocols, defining variables of interest, and establishing control groups. Once data is collected, you will perform comprehensive data analysis, applying statistical techniques and visualization methods to uncover meaningful insights.

- Documentation and Reporting: Throughout the project, you will maintain detailed documentation of experimental procedures, data preprocessing steps, algorithm development, and analysis methodologies. You will also contribute to the preparation of progress reports, research papers, and presentations summarizing the findings and outcomes of the project.

- Collaboration and Communication: You will actively participate in team meetings, discussing ideas, sharing progress updates, and seeking feedback. Collaboration with other researchers and

Skills required:
Possessing all of the skills listed below is not a prerequisite but can be beneficial.
- Understanding of Physiology: familiarity with physiological concepts related to emotions, stress, and engagement.

- Signal Processing and Data Analysis: familiarity in signal processing techniques and data analysis methods (e.g., filtering, feature extraction, time-series analysis). Experience with tools like MATLAB, Python or other relevant software packages.

- Machine Learning: familiarity with machine learning and deep learning and MATLAB or Python libraries (e.g., TensorFlow). Knowledge of computer vision libraries (e.g., OpenCV) will be beneficial.

- Programming Skills: Proficiency in programming with Python or MATLAB.

Research area: Membrane Science and Nanotechnology

Student roles:
1. Student will be part of a positive, productive, and creative research team, as well as attend group meeting, collaboratively learn from other group members, and share ideas.
2. Student will be trained to use the coaxial electrospinner and applying several approaches and collectors at my lab in order to control the membrane’s morphology. Students will be provided with guidance, research directions, and an outline for all of the requested tasks each week.
3. Student will test the influences of several parameters and their respective effects on on controlling membrane morphology.
4. Student will perform FO filtration experiments.
5. Analyze the results, collected data, and create the necessary models.
6. Student will have a weekly meeting to discuss the experimental results and will be provided with a memo for the following week.
7. Student will write a manuscript about the research findings while under my guidance.
8. Once the experiments are completed and analyzed, student will present the findings at a conference.

Skills required:
This project requires a background in Organic Chemistry and chemical engineering, as well as a background in experimental design, and modeling experience.

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:
Key responsibilities include gathering and analyzing data on the current energy consumption patterns of industrial robotic systems, which involves working with sensors and data acquisition systems to monitor energy usage. The student will assist in the development and implementation of optimization algorithms designed to reduce energy consumption in robotic operations, requiring proficiency in programming languages such as Python, MATLAB, or similar. Additionally, they will use simulation tools to create and test models of energy-efficient robotic systems, necessitating familiarity with simulation software and the ability to interpret and analyze simulation results.
The student will also evaluate different hardware components and configurations to identify potential improvements in energy efficiency, which may involve hands-on testing and experimentation with robotic systems. Conducting a thorough review of existing research and technologies related to energy efficiency in robotics, the student will summarize findings and identify potential areas for innovation. Documentation of all research activities, methodologies, and results is essential, as is preparing detailed reports and presentations to communicate findings to the research team and industry partners. The intern will work closely with a multidisciplinary team of researchers, engineers, and industry professionals, participating in regular meetings to discuss progress, challenges, and future directions of the project.
The ideal candidate should have a strong background in robotics, mechanical or electrical engineering, computer science, or a related field. They should possess analytical skills, experience with programming and simulation tools, and a keen interest in sustainable technologies. Good communication skills and the ability to work collaboratively in a team environment are also essential.

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: 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:
During the internship, the student will act as a junior graduate researcher, fully integrated into our lab's culture. Key responsibilities include:
- Conducting a targeted literature review on pain and VR UX.
- Developing the hardware-software integration linking a TENS unit to Unity via microcontrollers.
- Interacting with the research ethics board
- Designing, pilot-testing, and executing a formal user study to collect UX data.
- Performing statistical analysis on the collected data.
- Assisting in drafting an academic publication.
- Actively participating in weekly group meetings to present progress and exchange ideas with peers.

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

Preferred: Experience with hardware-software integration, embedded systems, or microcontrollers (e.g., Arduino). Exposure to HCI user studies and UX evaluation methodologies.

Willingness to Learn: Highly motivated to learn statistical data analysis, safely integrate physiological stimuli (TENS), and assist in scientific writing within a multidisciplinary lab environment.

Research area: Dr. AbdelRahman's research focuses on the structural behaviour and seismic response of masonry and concrete structures. His research experience includes behavior of masonry and concrete structures, large scale experimental testing, analytical studies, seismic behaviour of reinforced masonry walls and buildings, high-fidelity numerical modelling, 3D-printed concrete, and assessment of element- and system-level performance of buildings. Dr. AbdelRahman’s research has been published in peer-reviewed leading journals, such as Engineering Structures, ASCE Journal of Structural Engineering, Journal of Building Engineering, and Construction and Building Materials.

Student roles:
The Globalink Research Intern will play an active role in supporting the research on the structural behaviour of 3D printed concrete walls. Their responsibilities will be flexible and assigned based on their background, interests, and strengths. The role may include:
• Conducting literature reviews: Reviewing recent research on 3D printed concrete, structural behaviour, and modelling techniques to support the project’s methodology.
• Creating and modifying geometric models: Preparing CAD models of wall geometries for printing or simulation.
• Developing numerical models: Assisting in building, refining, and running finite element models to simulate the behaviour of 3D printed concrete walls under axial loading.
• Analyzing simulation results: Interpreting stress distributions, load–displacement responses, and failure patterns from analytical or numerical outputs.
• Supporting experimental validation: Helping prepare test specimens, set up instrumentation, and assist with data collection during laboratory testing.
• Processing and organizing data: Cleaning, plotting, and summarizing experimental and numerical data using EXCEL, MATLAB, Python, or similar tools.
• Contributing to documentation and reporting: Preparing figures, summarizing findings, and assisting in drafting sections of technical reports or publications.
• Participating in research meetings: Sharing progress, discussing challenges, and collaborating with the research team to refine approaches.

Skills required:
Applicants should preferably have some of the following background or skills:
Structural analysis fundamentals: Understanding of axial and flexure loading, stress–strain behavior, failure modes, and basic reinforced concrete mechanics.
Familiarity with concrete behavior, mix design basics and mechanical properties.
Ability to use Microsoft Office tools, MATLAB, or similar tools for data processing.
CAD and geometry modeling: Experience creating or modifying wall geometries in CAD tools for simulation or printing.
Experimental testing exposure: Prior lab experience with material or structural testing
Ability to summarize results, prepare figures, and contribute to reports or publications.
Attention to detail and research mindset

Research area: My research addresses nonlinear vibrations, nonlinear damping, and active vibration control. Using innovative experimental methods based on feedback-controlled excitation and laser vibrometry I measure large-amplitude vibrations in thin-walled structures and describe them with reduced-order models. This method has enabled ground-breaking studies on composites, rubbers, nuclear core components, and human arteries. I now aim to uncover how natural structures, through nonlinear elasticity and damping and frequency-dependent behavior, efficiently harness and dissipate energy under dynamic loads—advancing both fundamental science and engineering applications in materials and biomechanics.

Student roles:
Directly supervised by me, the student will play an independent role in the manufacturing of an MR prototype. The student will use CAD software to design the necessary molds. Afterwards, the molds will be manufactured either by the student using 3D printing technologies (for which infrastructure and training are available) or by professional technicians in the workshop if machining is required.

Based on previous experience, the student will prepare a mixture of magnetic powder and elastomer matrix, and will cast and cure this mixture under vacuum conditions (again, dedicated infrastructure and training are available). The student will then design a suitable electromagnetic coil or an arrangement of permanent magnets to activate the MR material.

Subsequently, the student will characterize the sample through static tests (e.g., slow traction tests) and dynamic tests (e.g., dynamic traction tests, modal vibration analysis, nonlinear vibration analysis, and hysteresis curve measurements). If feasible, the experimental data will also be reproduced using a simple numerical model (for example, a finite element model) developed by the student.

Skills required:
Ideally, the student should have a background in solid mechanics, dynamics, or materials science. Desirable experimental skills include elastomer molding and casting, 3D printing, and static and dynamic testing (e.g., traction testing, hysteresis measurement, modal analysis, and nonlinear vibration analysis). On the theoretical and numerical side, desirable skills include finite element modeling, vibration theory, and the use of Matlab or Python for data processing.

Research area: My research focuses on thermal energy storage, heat transfer, and sustainable energy systems. The work combines experimental testing, numerical modelling, and system-level analysis to improve the design and operation of thermal storage technologies. Applications include building energy systems, industrial waste heat recovery, renewable energy integration, and high-efficiency heating and cooling systems.

Student roles:
The student will participate in both the experimental and numerical parts of the project. The main tasks will include reviewing the literature on water-based thermal energy storage systems, preparing the experimental setup, conducting controlled charging and discharging tests, collecting temperature and flow data, and analyzing the results.

The student will also contribute to the development or improvement of a numerical model of the storage tank. This may include defining the geometry, setting up the governing equations, applying boundary conditions, performing simulations, and comparing the numerical results with experimental measurements. The student will help identify the main factors affecting thermal stratification, storage efficiency, and heat losses.
Throughout the internship, the student will meet regularly with the supervisor and research team, present progress updates, and prepare a short final report summarizing the methodology, results, and recommendations. The student may also contribute to the preparation of figures, tables, or preliminary material for a future conference paper or journal article.

Skills required:
The student should have a background in mechanical engineering, energy engineering, or a related field. Basic knowledge of heat transfer, thermodynamics, and fluid mechanics is required. Experience with MATLAB, Python, COMSOL, ANSYS Fluent, or experimental measurements is an asset. The student should be motivated to work on both experimental and numerical tasks.

Research area: The University of Northern British Columbia (UNBC) in Prince George, Canada, seeks qualified candidates to advance the applications of Reinforcement methods in Timber Engineering. The research aims to offer robust and practical design guidance considering gravity and seismic loads. Heightened public awareness regarding carbon footprints has dramatically increased the demand for sustainable construction and has initiated resurgence in the use of wood in tall residential and non-residential buildings. Together with the development of new engineered wood products and advanced connectors, wood-based hybrid structures that combine wood with different materials represent tremendous potential.

Student roles:
The student will be involved in the sampling, fabricating, testing and analyzing of timber test specimens. This will involve selecting test samples from a larger batch of material, using hand and machine tools to cut wood specimens, using advanced materials test equipment to carry out experiments, and applying spreadsheet tools and statistical programs to analyse the results. While previous knowledge and experience in some of these areas is expected, the student will be given all necessary guidance to acquire the necessary skills to successfully contribute to the research project.

Skills required:
The intern should have a background in either civil engineering, materials science, or a related field.
The candidates further need to meet following criteria:
• Interest in timber engineering;
• Willingness to work in a laboratory environment;
• Demonstrated proficiency in written and spoken English.

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser biostimulation to improve seed viability and resilience.

Student roles:
The student will contribute to a feasibility study on the development of a digital twin framework for grain storage monitoring and decision support. The student will review the literature on grain storage systems, digital twin technologies, sensor-based monitoring, predictive modelling, and decision-support tools. This review will help identify the current state of the art, major technical gaps, and opportunities for applying digital twin concepts to stored grain management.

The student will identify the key variables required for a grain storage digital twin, including temperature, relative humidity, grain moisture content, airflow, ambient weather conditions, storage duration, bin geometry, and potential quality or spoilage indicators. They will also compare possible sensing technologies, such as temperature cables, humidity sensors, moisture sensors, CO₂ sensors, acoustic sensors, thermal imaging, and other relevant monitoring tools.

The student will evaluate different modelling approaches that may be suitable for a grain storage digital twin, including physics-based models, data-driven models, machine learning models, and hybrid approaches. If suitable existing or simulated datasets are available, the student may conduct preliminary data analysis, such as trend analysis, anomaly detection, spoilage-risk estimation, or basic predictive modelling using Python, MATLAB, or similar tools.

The student will also help develop a conceptual digital twin architecture showing how sensor data, data processing, predictive models, visualization tools, and decision-support outputs could be connected. The student will document all findings, prepare progress updates, contribute to the final report, and may assist with preparing a conference abstract, manuscript, or future grant proposal. The student will work under supervision while also being expected to take initiative, organize technical information clearly, and collaborate with the research team.

Skills required:
The student should have a background in biosystems engineering, agricultural engineering, mechanical engineering, computer science, data science, electrical engineering, food science, or a related field. Basic programming skills in Python, MATLAB, or a similar platform are required. Experience or interest in data analysis, sensors, modelling, machine learning, Internet of Things technologies, or agricultural systems would be beneficial. Familiarity with grain storage, postharvest engineering, digital twins, process modelling, or decision-support systems is an asset, but not required. The student should be able to conduct literature reviews, organize technical information, communicate clearly, work independently, and collaborate effectively with the research team.

Research area: My specialized research area within Computer Science revolves around Security, Privacy, and the Internet of Things. Mostly, I would say I focus on data related issues and the issues that related to the sharing of data amongst stakeholders. I am specifically trained in Cryptography, Security and Privacy and Social Networks. I look at applying cryptographically secure protocols to real-world settings. I have extensive research background and publications in Social Network Security and Privacy, Cryptography, Blockchain Technology, and Internet of Things (IoT) as of late. My main focus is to provide secure options to systems and protocols that would otherwise lack

Student roles:
The student will assist in all aspect of this project based on their capability. Currently, we are only at the preliminary stages of this project. We would expect the student firstly to take on a thorough literature search over weeks 1-2 to better understand network influence and network analysis. In weeks 3-6, the student will be expected to assist with some aspects of the collection of data for this project and also the design of the recommended experiments. This is where some data mining background would be helpful. In the weeks 7-10, some evaluation will take place of the collected data which will require the student to assist in analyzing the data collected based on a set of measures including but not limited to Accuracy, Precision, F1-Score, then on the network side Latency and Throughput. In this phase we may also create our own unique novel measures to compare to existing ones. Finally, in weeks 11-12 we can expect the student to assist with some of the technical writing involved in any research manuscript that would be developed over the course of their project.

Skills required:
The student will need some exposure to Artificial Intelligence and Federated Learning or a basic understanding of Artificial Intelligence . A desire to learn more about Artificial Intelligence, Federated Learning, security, privacy, cybersecurity is important as well. A good mind for organizational tasks and a strong foundation in Mathematics is a must.

If the student is good at writing it is a bonus. We will teach the student LaTex, so the student must possess a willingness to learn this. The student will be working with others, so communication and interpersonal skills are helpful. Independence can also be a key skill

Research area: In the Adaptive Bionics Lab, we research the design of quasi-passive prostheses and exoskeletons that adapt to speed, terrain, and ground surface for walking and running optimization. Our research merges precision machine design, biomechanics, and robotics for the development of new types of prostheses and exoskeletons and evaluation of gait. Within this largely understudied area of wearable robotics research, we develop devices that adjust joint stiffness, position and/or dampening in order to mimic important physiological behavior of biological lower limbs. We study the biomechanics of human gait and how our developed devices may improve walking.

Student roles:
The Mitacs Globalink intern will collaborate with researchers in the lab on this project, with a focus on simulation development and evaluation. The student will develop and optimize FEA pipelines, collect data, analyze data, and interpret results. The student may also assist with biomechanical evaluation with human participants using motion capture and force plate analysis, and assist with processing the collected data. The intern will be exposed to the tools and methods we use in the lab, including metabolic analysis, motion capture, biomechanical evaluation, material characterization, and finite element analysis.

Skills required:
This project is suitable for a student with a strong background in mechanical or biomedical engineering. A strong foundation from undergraduate engineering courses in physics, dynamics, and statics is ideal. The student should be familiar with reading scientific articles in engineering or medicine. Prior experience in Patlab and Python is preferred. Experience using FEA software such as Abaqus, Solidworks, or Ansys is preferred. A successful student should be able to work independently with guidance and mentorship from graduate students and professors.

Research area: My expertise for this project is real-time systems, which in this case is applied to the processing of electrophysiological signals. My interest is in developing a fast feedback loop for incoming EEG signals generating auditory stimulation.

Student roles:
The student will improve the Portiloop, a small signal processing system that collects input from a portable EEG system and identifies brain waves at a certain frequency. The system then output stimulation that is synchronized with the detected waves.

Skills required:
Python, Linux and a general knowledge of embedded systems

Research area: My research area is focused on the exciting aspects of brain circulation and circuitry. I have extensive experience in brain imaging with MRI; and as an electrical and biomedical engineer, I work to expand the understanding of brain circuitry and circulation. I am of the belief that this will yield notable new advances in machine learning. I use advanced data analytics, supercomputing and machine learning regularly in my research; and my trainees tend to be very skilled in these areas. I work with the MRI for data acquisition. I also have procured large databases for testing associations from brain images.

Student roles:
These internships are oriented towards the development of software that can be used towards the research program aims. The trainee will be required to develop software and commit their changes to a code repository. The student is expected to meet for research team meetings, and one-to-one meetings with the supervisor weekly. We work in a dynamic team environment on many projects, so interacting with the other lab members is usually helpful.

The students have the opportunity to work with the more senior graduate students, and this will help them to get a better sense of the what a research career might have for them. There are many successful graduate students at the University of Calgary who have previously come on the Mitacs GlobalLink Program. It is a really great chance to make a small contribution to research, while learning a lot and visiting a new place.

Our lab is fun and good spirited. Calgary is a city of greater than one Million people on the south-western edge of the Canadian Prairie, it is located in the foothills of the Canadian Rocky Mountains, which includes vast National Parks and Wilderness. These regions are home to world class Skiing and Hiking. Calgary is a diverse city with many activities. Although known for wealth from the oil industry, Calgary is focused on remaking itself as a high tech hub, with many emerging bio and tech companies. Calgary has beautiful rivers and pathways throughout. Calgary is the sunniest city in Canada with an average of 333 days per year! Canada is known for it inclusive and diverse society, and we aim for our lab to have a similarly kind atmosphere. I strongly encourage applicants who might have a research interest in this area to seize this internship opportunity and enhance your skills.

Skills required:
An ideal student should have a demonstrated interest in a technical domain, such as: Biomedical, Electrical, Computer, or Software Engineering; applications from candidates in (Medical) Physics and Computer Science can also be a good fit for this research program and will be considered. Experience in computing and programming is desirable. Some experience in machine learning, computer vision, or medical imaging processing would be ideal. Motivation, a good attitude and ability to work with others is required. Primarily, we are looking for students who have an interest in graduate studies in this research area.

Research area: My research activities include : (i) binder technology for road & construction materials, (ii) carbon capture studies for binders, (iii) adsorption studies of novel regenerative hybrid magnetic SBA 15, (iv) characterization and remediation techniques of problematic soils, (v) effects of temperature and pore fluid salinity on clay barriers, (vi) mechanical behaviour of clay barriers at different suction levels and (vii) smart design of permeable reactive barriers. I have been working on experimental techniques and therefore, have established a successful hands-on HQP training program in my lab.

Student roles:
The research laboratory has advanced testing facilities which require special training to operate. The student will be trained to use the equipment and provided with the necessary background knowledge to carry out research according to protocols laid out by primitive researchers. The student will work with a team of scholars to share resources, exchange ideas and scientific concepts, as well as assisting other students when it is required. Time management skills are essential as this position involves determined milestones and several interactions with the industrial partners. The student will work within clear deadlines for completing deliverables. The student will also work on collecting experimental data, statistical analyses of data sets, and computer programs related to the research field. The objective of the project is clearly defined for the student; however, the student will have opportunities to develop protocols that will work best for carrying out the design process. This will encourage the student to become involved in a teamwork setting and find synergy among their skills and capabilities.

Skills required:
The intern needs to have a great perseverance and enthusiasm for the sake of the research project. Besides, the student needs to have high levels of communication and time management expertise and the ability to work independently and within a team. The student should also have general problem solving and multitasking skills as well as a motivation for learning new technologies to have a satisfactory progress in their assigned project along with courses. Furthermore, the student is required to attend weekly research group meetings and to protect confidential research outcomes of the project.

Research area: Graph neural network, Smart grid, Transportation systems, Reinforcement learning, and Mathematical modelling

Student roles:
This project will host two students who collaborate closely throughout the 12-week internship, working with the supervisor and graduate students.
Student A – Graph neural network model: This student will model the coupled smart grid and transportation system as a heterogeneous graph, defining node and edge types and their features, and implement a heterogeneous graph neural network to capture the inter-system interactions, including extract and preprocessing representative datasets and validating the learned representations.
Student B - Problem formulation. This student will formulate the electric vehicle fleet management problem as a mathematical optimization that minimizes charging operation time at large fleet charging depots while respecting grid and operational constraints, then translate the formulation into a tractable form suitable for a learning-based solution.
Joint work by two students: Building on the graph model and formulation, both students will develop and train a reinforcement learning algorithm for real-time fleet management, designing reward functions, running experiments, and benchmarking against heuristic baselines.

Skills required:
The ideal candidates are senior undergraduate students in computer science, electrical engineering, applied mathematics, or a related field. Required: strong Python programming and a solid grasp of machine learning fundamentals. Familiarity with graph neural networks and deep learning frameworks (PyTorch, PyTorch Geometric, or TensorFlow) is highly desirable, as is exposure to reinforcement learning. Background in optimization or mathematical modelling, and interest in smart grid or transportation systems, are strong assets. The candidate should be analytical, self-motivated, and able to work both independently and in teams. Working proficiency in English is required.

Research area: My specialized research area is green cellulose materials and products to replace single-use-plastics for packaging applications.

Student roles:
1. Preparing coating formulation in the lab;
2. Carrying out coating experiments in the lab;
3. Testing water vapor transmission rate of the cellulose films;
4. Testing oxygen gas transmission rate of cellulose films;
5. Data processing
6. Report writing.

Skills required:
Chemistry, Chemical Engineering, or Mechanical Engineering.

Research area: Dr. Domenico Santoro's research focuses on advanced water treatment technologies, particularly in disinfection, biosolids treatment, and resource recovery. He specializes in applying computational fluid dynamics (CFD) and multi-scale modeling to optimize water treatment processes, including advanced oxidation for wastewater reuse. Dr. Santoro leads international collaborations with academic and industrial partners to innovate in water treatment technologies. His work spans areas such as ozonation, biosolids processing, and the development of sustainable water treatment practices. He is also involved in the development of patents and optimization strategies for water treatment systems .

Student roles:
Analytical Chemistry (1 Student):
This student will monitor water quality in hydroponic systems, analyzing nutrient levels (e.g., nitrogen, phosphorus) and contaminants in wastewater before and after treatment. They will use analytical techniques such as HPLC, ion chromatography, and spectrophotometry to quantify nutrients and assess plant uptake efficiency. Their role includes evaluating the interaction between wastewater components and plant growth, ensuring nutrient removal is effective while supporting plant health. They will collaborate with the engineering team to interpret results and refine the system design.

Chemical/Environmental Engineering (1 Student):
The student in this role will design and optimize hydroponic systems integrated with wastewater treatment. Their responsibilities include selecting plant species, adjusting system parameters (e.g., flow rates, nutrient concentrations), and ensuring efficient nutrient removal while promoting plant growth. The intern will also explore the use of biofilters and other biological treatment methods to enhance nutrient uptake. They will work with real wastewater to assess system performance and make improvements to optimize both water treatment and crop production, ensuring the system's scalability and sustainability.

Physics/Applied Mathematics (1 Student):
This student will develop mathematical models to simulate nutrient removal, water flow, and plant growth in the hydroponic system. Using tools like MATLAB or Python, they will model nutrient dynamics, plant uptake, and optimize system parameters for maximum efficiency. The student will validate models with experimental data and use simulations to guide design adjustments. Their role includes improving system performance, helping design experiments, and supporting the team with computational insights to enhance nutrient management and resource recovery.

Skills required:
Analytical Chemistry (1 student): Knowledge of water quality analysis, including nutrient and contaminant detection (e.g., nitrogen, phosphorus) using techniques like HPLC, ion chromatography, or spectrophotometry.

Chemical/Environmental Engineering (1 student): Expertise in designing and optimizing hydroponic systems, knowledge of wastewater treatment, plant nutrition, and resource recovery.

Physics/Applied Mathematics (1 student): Proficiency in modeling hydroponic systems, nutrient dynamics, and system optimization using tools like MATLAB or Python. Strong foundation in applied mathematics for process simulations and performance enhancement.

Research area: My research group develops high-performance computational models to predict the complex behavior of advanced materials in extreme environments, such as those used in nuclear power and clean energy. We focus on multiscale modeling, integrating bulk thermodynamics and multiphysics transport to understand material performance from the nanoscale up. By building mathematically robust, open-source software tools—like phase-field codes and thermodynamic data engines—we aim to accelerate the design and qualification of novel materials for a carbon-neutral economy.

Student roles:
The student will play a critical role at the intersection of computer science compiler theory and computational materials science. Working closely with the supervisor and the research team, the intern will focus on building and benchmarking the bridge between the ThermoGraph frontend and the ZNet execution backend.

Specific responsibilities include:
- Blueprinting Physics: Developing stateless PyTorch modules for specific thermodynamic physics generators (e.g., constant reference energies, polynomial subgraphs).
- Compiler Assembly: Writing Python code to automatically map the human-readable ThermoGraph Abstract Syntax Tree (AST) into the ZNet executable Directed Acyclic Graph (DAG), ensuring static configuration matrices are cleanly indexed for hardware execution.
- Benchmarking and Validation: Validating the tensor contractions over dense, multi-dimensional grids (e.g., thousands of composition and temperature combinations) to test execution speeds against traditional iterative solvers.

Beyond coding, the student will meticulously document their software architecture and contribute to publishing the modules as open-source tools. The intern will participate in weekly research group meetings, where they will present their progress, engage in collaborative problem-solving, and refine their technical communication skills. This role offers hands-on experience in AI-adjacent high-performance computing and the development of robust scientific software.

Skills required:
The ideal candidate will have an academic background in Computer Science, Engineering Physics, Materials Science, or Mathematics. Strong proficiency in Python programming is required. An eagerness to learn or prior experience with high-performance tensor frameworks (specifically PyTorch) is highly desirable, as the project relies heavily on tensor broadcasting and compilation. Foundational knowledge of compiler theory (Abstract Syntax Trees, directed acyclic graphs) or thermodynamics (multiphase equilibria, chemical potentials) is a strong asset. The student should possess creative problem-solving skills and an interest in bridging software engineering with computational physics.

Research area: My specialized research area lies in wireless communications and networking, with a particular focus on Integrated Sensing and Communications (ISAC). My work centers on optimizing signal processing techniques, waveform design, and the joint development of communication and sensing functionalities. The goal is to achieve seamless integration and enhanced performance, enabling more efficient, multifunctional wireless systems suitable for next-generation applications such as 6G networks, smart environments, and autonomous systems.

Student roles:
1-Contribute to the conceptual design and rigorous mathematical modeling of an RFID localization system utilizing flexible antenna technology to enhance accuracy and adaptability.

2-Develop and implement AI-based algorithms (e.g., reinforcement learning (RL) or optimization-based control) for dynamic antenna orientation and beam steering.

3- Evaluate the RFID localization accuracy improvement under varying environmental conditions.

4-Document the methodology, results, and conclusions

Skills required:
To successfully undertake the proposed research project, the student should possess good knowledge in Wireless Communications and Signal Processing with solid background in linear algebra, probability theory, and optimization. The student should also have good experience in programming (e.g., MATLAB, Python) and AI frameworks such as TensorFlow, PyTorch.

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser or LED biostimulation to improve seed viability and resilience.

Student roles:
Up to four interns may participate in this project, each working on one crop type. The role of each student includes
1. Sample preparation to induce mechanical damage as described in [1].
2. RGB or 2D X-ray image acquisition [1-2].
3. Machine learning analysis of images using MATLAB or Python.
4. Documentation and reporting.
References:
[1] Nadimi, M., Divyanth, L. G., & Paliwal, J. (2023). Automated detection of mechanical damage in flaxseeds using radiographic imaging and machine learning. Food and Bioprocess Technology, 16(3), 526-536.
[2] Nadimi, M., Loewen, G., & Paliwal, J. (2022). Assessment of mechanical damage to flaxseeds using radiographic imaging and tomography. Smart Agricultural Technology, 2, 100057.

Skills required:
The student should have a background in engineering, computer science, agriculture or a related field. Basic skills in programming (preferably MATLAB or Python) and data analysis are needed. An interest in imaging, sensors, or agriculture is helpful. The student should be willing to learn, able to follow experimental procedures, and work well both independently and in a team. Previous experience with image processing, machine learning, or lab work is a bonus.

Research area: Bimanual robotics, 3D perception, machine learning and affordances for robotic manipulation. The laboratory works with a Kinova Gen3 two-armed robotic platform and a software environment based on ROS2 and MoveIt2. The data studied can include 3D point clouds, simulated objects, interaction regions and geometric representations from existing methods.

Student roles:
The intern’s role will be to identify existing methods relevant to affordances in bimanual robotics, and then select one or more with the supervisor. They will have to assess their availability, dependencies, inputs, outputs, limits, and compatibility with 3D data.

The work will include the installation, adaptation or relocation of at least one reference method, as well as tests on simple, synthetic, public data or data provided by the original articles. The trainee must produce clear documentation to understand how to perform the method, what data are needed, what results are produced and what limits have been observed.

At the end of the internship, he will have to provide a comparative table of the studied methods or the functional code, examples of use and a report on the relevance of the method for future experimental comparisons.

Skills required:
The candidate should have a basic knowledge of Python or C++, machine learning, computer vision and point cloud processing. Experience with PyTorch, ROS2, GitHub or the reproduction of scientific articles would be an asset. An ability to read research articles and document code is desired.

Research area: Her research lies at the interface of materials science, structural engineering, and data science. She focuses her work on the use of infrared thermography in civil engineering, advanced non-destructive structural monitoring, and the development of reliable and intelligent tools for infrastructure assessment and durability. She develops innovative approaches grounded in physical and engineering principles, integrating artificial intelligence, BIM, and digital twins for predictive maintenance and the development of smart and resilient infrastructure.

Student roles:
Selected students will contribute to an ongoing research program on the non-destructive assessment of reinforced concrete infrastructure. Depending on the assigned stream, the student will support experimental work, data analysis, and/or digital processing of inspection data.

Responsibilities include preparation of concrete samples for laboratory testing, assistance in experimental measurements, and processing and interpretation of data to relate NDT responses to underlying deterioration processes in reinforced concrete. Students will also contribute to literature review activities, helping to contextualize results within current state-of-the-art research.

For students in the digital-oriented stream, the role additionally includes supporting data processing workflows, basic implementation of change detection methods, and contributing to the organization and visualization of inspection data using programming tools such as MATLAB or Python.

Across all streams, students are expected to participate in research discussions, maintain clear documentation of their work, and contribute to the development of reliable, data-informed approaches for infrastructure assessment.

Skills required:
Applicants should have a background in civil engineering or a closely related engineering discipline, with a solid understanding of reinforced concrete behavior and structural engineering fundamentals.

The ideal candidate is motivated by experimental research and data-driven approaches to structural assessment. Strong analytical skills, attention to detail, and the ability to work both independently and within a research team are essential.

For the digital/data-oriented stream, basic experience with programming (e.g., MATLAB or Python) and data analysis is required.

Research area: Additive manufacturing (AM) promises to lead the next industrial revolution. Tremendous progress has been made in 3D printing, printed electronics and bio printing to fabricate mechanical, electrical and biological components. Some of the most exciting applications combine two or even three of these different areas of additive manufacturing. For example, medical devices such as implants or prostheses that are customized to each patient could be printed containing sensor networks using quantum materials. So far, these different areas of AM research have been mostly isolated from each other. The ultimate goal of this project is to integrate them.

Student roles:
The student will work in a chemical microfabrication lab to carry out experiments. The student will own his or her process performing all steps themselves including the formulation of inks, the printing of patterns, the fabrication of test devices and the testing of the fabricated materials and devices. The student will optimize processes and study relevant variables that give insights and performance improvements. The student will receive the necessary training on lab techniques, equipment and processes to allow him or her to become an independent researcher in the lab even without prior lab experience. The student will need to carefully analyze the results he or she collects in the lab to develop new ideas for the next experiment. Before starting the internship, as well as throughout, the student should read relevant literature to understand his or her results in the wider context. One goal, although ambitious for a short summer internship, is for the student to write up his or her results and submit them for publication. This will not only serve to improve the student’s chances when applying for graduate school but also help them gain valuable experience and skills in writing and communicating their scientific results to a wider audience. In all of these steps the student will receive supervision and guidance to allow him or her to grow over time and become more independent.

Skills required:
Since additive manufacturing and printed electronics is an interdisciplinary field, students from a variety of backgrounds will be able to make an impact. Areas that are particularly relevant are electrical engineering, fluid mechanics, mechanical engineering, materials science, chemical engineering and chemistry. The project can be tailored towards the student’s interest and experience but the interdisciplinary nature of the research is also a chance for the student to learn about areas outside of their current expertise and to broaden their horizon. The student will learn any relevant skills and knowledge during the internship.

Research area: I specialize in modelling and automating complex systems. Specifically, my background is in the development of Model Predictive Control methodologies for Multiple Input Multiple Output systems that display characteristics such as non-linearities and time variance, which are challenging to manage automatically. To achieve this, I have developed and supported studies involving signal processing, intelligent sensing, system modelling, and controls, leveraging modelling tools ranging from traditional ordinary differential equations to contemporary data-driven methods, such as Machine Learning, Reinforcement Learning, computer vision, and more.

Student roles:
The intern will be expected to come to the laboratory in person on most days, with the flexibility to work remotely on occasion. They will participate in biweekly lab meetings, present their findings, and collaborate with graduate students to integrate their work into the MAM-Lab culture. They will spend the initial part of their internship familiarizing themselves with their predecessors' work. They will then make a plan with the lab director early in the internship to set goals that align with their skill set and abilities, focusing on knowledge transfer and documentation during the final weeks of the internship.

Skills required:
Some skills that would be an asset for the students include an interest and knowledge in automotive systems, time management skills, as well as experience with simulations in Unity, MATLAB, or other software. Creative, self-motivated students will find this opportunity rewarding, since the project is in its initial stages, there may be flexibility in project direction to match an intern's skill set. For example, this project can remain in a simulated environment; however, if a student has skills in mechatronics and chooses to focus on validation with a physical prototype, this could be supported.

Research area: Research Interests: Fate and transport of emerging contaminants Environmental risk assessment Environmental sustainability Volatile organic compounds (VOCs) transport in soil and remediation Exploration of the use of various waste materials for environmental application Utilization of wastes to fuel Development of remediation technologies for clean-up of contaminated soil Development of treatment technologies for acid rock drainage Transportation and highways pollution: air, soil and runoff Wildfires and impacts on water quality Chemical compatibility studies of clay liners Mine tailings reduction, containment system, treatment and control Waste (municipal, mineral, industrial and hazardous) management and land reclamation

Student roles:
The students will use a combination of governmental databases and a modelling software to collect data, synthesize and interpret data and make thermodynamic predictions. Below is the expected role. Training and close mentorship will be provided
1. Use literature to identify representative fire-prone watersheds across Canada with sufficient wildfire history and long-term water quality monitoring records.
2. Use the Canadian National Fire Database to identify significant wildfire events based on burned area, burn severity, duration, and proximity to monitored water bodies.
3. Use meteorological records to identify major precipitation events following wildfires that may promote ash deposition, runoff, erosion, and contaminant transport.
4. Use the Environment and Climate Change Canada (ECCC) long-term water quality monitoring database to characterize pre-fire (i.e. baseline) and post-fire water quality at multiple monitoring locations within affected watersheds, accounting for spatial and temporal variability. Baseline data will be established using pre-fire historical data from the same season over multiple years.
5. Use published studies on wildfire-water quality response times, including differences between riparian and more distant fires, to identify post-fire monitoring periods most likely to capture wildfire impact
6. Use the National Pollutant Release Inventory (NPRI) to identify potential industrial contamination sources and distinguish, where possible, wildfire-related impacts from anthropogenic influences.
7. Use PHREEQC (USGS geochemical modelling software) to predict contaminant speciation under baseline (pre-fire) and post-fire conditions. Modelling will use available water quality parameters from ECCC and, where necessary, incorporate representative wildfire ash compositions from the literature to evaluate potential changes in water chemistry. Additional calculations may be used to assess contaminant fate processes that are not explicitly represented in the PHREEQC modelling framework

Skills required:
Expected Student Skills:
1. Knowledge on water quality parameters and thermodynamics
2. Able to use databases and handle large data using coding or Excel skills
3. Environmental and geochemistry knowledge
4. Able to use software, without supervision, for data processing, graphics, word processing.
5. Preferred to have familiarity with PHREEQC or other thermodynamic software

Research area: In my research, I investigate how geometry influences the response of materials, robots, and structures. I exploit nonlinear phenomena found in folding origami motifs, cutting kirigami patterns, buckling elastic beams, and including defects in the topology of lattices. I develop theoretical, numerical, and experimental tools to tune and apply these phenomena in the fields of (1) multistable deployable structures; (2) instability-driven soft robots; and (3) 3D printed biomaterials.

Student roles:
The intern will be responsible for manufacturing prototypes at Polyfab using laser cutting. Their role includes developing Python scripts to generate complex non-uniform cutting patterns. They will conduct drop tests at the Atrium Trottier to quantify drop stability and carry out wind tunnel testing campaigns to measure aerodynamic forces. They will then analyze the collected data using Python and present their results to the LM2 group.

Skills required:
To conduct this research project, the ideal student should be creative, self-driven, familiar with computer aided design, programming, the finite elements method, and prototyping via 3D printing and laser-cutting.

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser or LED biostimulation to improve seed viability and resilience.

Student roles:
The student will contribute to the design, implementation, and analysis of LED-based biostimulation experiments for selected crop seeds or seedlings. The student will help prepare plant materials, set up LED treatments, apply controlled light exposures using selected wavelengths, intensities, and exposure durations, and maintain consistent growth conditions in the laboratory, growth chamber, or greenhouse.

The student will monitor plant responses after treatment, including germination percentage, germination rate, root and shoot growth, biomass, leaf area, and overall seedling vigor. Where applicable, the student will capture RGB images and use image processing tools to quantify plant growth traits and compare treatment effects over time.

The student will organize experimental data, perform statistical analysis, prepare figures and tables, and summarize the results clearly. They will also maintain detailed records of treatment conditions, growth conditions, experimental procedures, and observations. The student will prepare progress updates, contribute to the final project report, and may assist with preparing a conference abstract, manuscript draft, or future grant proposal.

The student will work under supervision but will be expected to follow protocols carefully, troubleshoot routine experimental issues, work independently when needed, and collaborate effectively with the research team.

Skills required:
The student should have a background in plant science, agriculture, biosystems engineering, biological sciences, environmental science, food science, or a related field. Basic knowledge of plant growth, germination testing, experimental design, and data analysis would be beneficial. Experience with laboratory work, growth chambers, greenhouse experiments, LED lighting systems, imaging, or plant phenotyping would be considered an asset.
The student should be comfortable collecting and organizing experimental data, measuring plant growth traits, and using tools such as Excel, statistical software, Python, or MATLAB for data analysis. Interest in sustainable agriculture, physical biostimulation, controlled-environment experiments, and image-based plant monitoring is desirable.

Research area: I work in computer vision and computer graphics, especially on problems lying at the intersection of the two. I am particularly interested in exploring how information automatically estimated from images (vision) can be used to generate novel, realistic images (graphics). In this context, we explore how large image and video generative models can be adapted to be given more control over the desired output. We leverage insights from computer graphics to achieve unprecedented realism in the generated results.

Student roles:
The student will have the opportunity to play a central role in the research activities. In collaboration with the graduate students in the lab, the main role of the student will be to design, implement, and test research code. The student will also have the opportunity to actively contribute to discussions during meetings with members of the research team, and thus provide valuable input that will help determine the direction of the research effort. He or she will also assist in capturing, gathering, and organizing data relevant to the research activities. The student will be asked to read, summarize, and discuss papers.

Past Globalink interns have contributed to the successful publication of research papers, and it is expected that the intern will also participate in writing papers this summer.

Skills required:
First and foremost, the student must be motivated, self-driven, and passionate about computer vision, graphics, and machine learning. In addition, the student is expected to be pursuing an undergraduate degree in computer engineering or computer science, or in related fields. He or she is expected to have a solid theoretical background in linear algebra, discrete mathematics, signal processing, probability, and optimization, as well as significant programming experience. Prior knowledge of computer vision, computer graphics, machine learning, and/or 3-D data processing is a big plus. Prior experience in research as well as in python programming are also strong assets.

Research area: Laser-resonance-ionization spectroscopy (RIS) is applied in ultra-trace analysis for highest selectivity and sensitivity. In our application at TRIUMF Canada's National Laboratory for Nuclear and Particle Physics, this technique (RIS) is used to extract clean beams of rare, short-lived radioactive ions for nuclear physics experiments. The research goal is to build and test a field ionization unit that allows to state selectively field ionize Rydberg atoms. This state selective ionization allows to push the detection limit of colllinear fast beam laser spectroscopy to a few atoms per second - for laser spectrospy measurements on rare radioactive isotopes.

Student roles:
Student is expected to learn the operation of experimental equipment and perform laboratory tasks independently. Will be working through a selection of supporting literature and textbook material, do literature research and give weekly presentations and progress reports on selected topics in atomic/molecular and optical physics topics related to the research performed. In parallel the student will experience work in the reaserch & development team at a large scale resaerch laboratory, i.e. TRIUMF's on-line isotope separator and accelerator facility. Supervised work with lasers and in radiation areas may be required. Training for these tasks will be provided.
The student is expected to take part in the TRIUMF coop student enrichment program which includes soft skills workshops, student seminar series etc.

Skills required:
Interest in experimental work, techniques and instrumentation. Interest in operating experimental equipment.
Good documentation skills. Some programming experience (python and/or C) would be beneficial. A background in atomic/optical/nuclear physics and electronics or interest in these areas would be beneficial.
Student should be open to ask questions and be able to work independently yet know when to ask for support. We provide an immersion into our research team with open discussions and hands on training. We do not expect the student to bring expert knowledge, but to leave with a solid understanding of the project.

Research area: My research develops trustworthy artificial intelligence for complex biomedical data. We move beyond simple correlations to build AI models that can understand cause-and-effect relationships. Our lab specializes in multimodal deep learning, causal inference, and graph neural networks to integrate genetic, imaging, and clinical data. The ultimate goal is to create interpretable, robust, and clinically actionable AI systems for disease mechanism discovery and personalized treatment planning.

Student roles:
The two interns will work as a coordinated research team under direct supervision of the PI and a senior PhD student. Weekly joint meetings will ensure alignment.

- Intern A Responsibilities:

1- Download and preprocess multimodal datasets (TCGA, LINCS)

2- Build data loaders for PyTorch with proper train/val/test splits

3- Implement the symbolic knowledge integration pipeline to import KEGG/Reactome pathways as graph constraints

4- Create data visualization dashboards for exploratory analysis

- Intern B Responsibilities:

1- Implement the GNN architecture with MASR regularization in PyTorch Geometric

2- Implement baseline causal discovery methods (CD-NOD, cVAEs)

3- Run experiments and benchmark performance using SHD and literature-based precision

4- Generate and visualize learned causal graphs

- Joint Responsibilities (Weeks 8-12):

1- Collaborate on ablation studies (e.g., with/without symbolic constraints)

2- Prepare final report and presentation together

3- Document codebase collaboratively using GitHub

This structure promotes peer learning while maintaining clear ownership. The project offers potential for co-authorship on a workshop or conference paper including ML4H, NeurIPS workshops, ACM BCB, and or IEEE BIBM.

Skills required:
Strong Python programming (PyTorch/TensorFlow) and foundational machine learning knowledge.
- For Intern A: Experience with data preprocessing (Pandas, NumPy) and biomedical data formats.
- For Intern B: Interest in graph neural networks or causal inference.
Both should be comfortable reading scientific literature.

Research area: The Dynamics and Digital Manufacturing (DDM) Research Laboratory conducts research at the intersection of dynamic systems and advanced manufacturing. The lab develops physics-informed and data-driven methods to model and optimize manufacturing processes and equipment. Research areas include machine tool dynamics, machining process modeling, robotic machining, vibration modelling, and modal analysis. Through close collaboration with industry and academic partners, the DDM Lab aims to advance sustainable and high-performance manufacturing technologies while training highly skilled engineers and researchers in digital manufacturing and automation.

Student roles:
1- Literature Review and Project Planning
Review imitation learning methods
Review examples of imitation learning in manufacturing and robotic manipulation.
Identify suitable methods for a short proof-of-concept implementation.
2- Demonstration Data Collection
Design a simple data acquisition workflow.
Record carving demonstrations using Motion capture systems and/or Robot teaching modes, Handheld tracking devices
Process and synchronize trajectory data.
3- Motion Learning and Reproduction
Implement an imitation learning framework using Python.
Learn tool trajectories and orientations from demonstrations.
Generate smooth, reproducible trajectories suitable for robotic execution.
Visualize learned motion patterns.
4- Robotic Validation
Transfer generated trajectories to a robotic simulation environment or physical robot.
Compare reproduced motions against demonstrations.
Assess trajectory accuracy, smoothness, and repeatability.
5- Analysis and Reporting
Analyze strengths and limitations of the selected imitation learning approach.
Recommend future directions for integrating imitation learning into robotic carving workflows.
Prepare technical documentation and a final project report.

Skills required:
The ideal candidate is a senior undergraduate student in Mechanical Engineering, Electrical Engineering, Computer Science, or a related field.

Required Qualifications include the following:
- Strong programming skills in Python, C, or similar languages.
- Familiarity with linear algebra and basic data analysis.
- Interest in robotics, machine learning, or advanced manufacturing.
- Ability to work independently and communicate technical results effectively.
Preferred Qualifications include:
- Experience with ROS, robotic manipulators, or automation systems.
- Experience with machine learning libraries (e.g., PyTorch, TensorFlow, Scikit-learn).
- Familiarity with computer vision or motion capture systems.
- Experience with CAD/CAM or digital fabrication

Research area: The Toronto Intelligent Systems Lab develops machine learning methods for autonomous driving and intelligent robotic systems. Our research focuses on end-to-end autonomous navigation, closed-loop learning, reinforcement learning, simulation, and safety-critical evaluation. We are particularly interested in improving the robustness and generalization of autonomous driving systems by combining data-driven policy learning with advanced scenario generation and testing methodologies that expose vehicles to challenging and rare real-world situations.

Student roles:
The student will participate in all stages of the research process. Responsibilities may include reviewing relevant literature, reproducing and evaluating existing methods, implementing machine learning algorithms, developing simulation and evaluation tools, designing experiments, and analyzing results.

Depending on the selected project, the student may contribute to the development of end-to-end driving policies, reinforcement learning methods, closed-loop training pipelines, scenario generation algorithms, automated testing frameworks, or simulation-based evaluation methodologies. The student will work closely with graduate students in the lab, participate in regular research meetings, and contribute to discussions on experimental design and research direction.

Students will gain experience working with modern machine learning frameworks, autonomous driving datasets, and simulation platforms while developing skills in scientific communication and research methodology. The project will be conducted with the goal of producing a conference or journal publication, and the student will actively contribute to the preparation and writing of the resulting manuscript.

Skills required:
The student should have strong programming skills in Python and familiarity with machine learning and deep learning frameworks such as PyTorch, including experience implementing and training modern neural network architectures (e.g., CNNs, Transformers, diffusion models, or reinforcement learning models). Knowledge of robotics, autonomous driving, simulation environments, or software development for research projects is an asset. Strong analytical skills and the ability to work independently while collaborating within a research team are expected. Strong Preference will be given to candidates with prior experience working with autonomous vehicle software stacks.

Research area: Our research focuses on converting waste materials—such as plastics and biomass—into valuable fuels and chemicals through thermochemical processes like pyrolysis. We integrate experimental research with process modeling and sustainability assessment to develop circular and energy-efficient solutions. By combining tools such as Aspen Plus for simulation, and Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) for system-level evaluation, we aim to identify viable waste-to-energy technologies. Our interdisciplinary work advances resource recovery, supports emissions reduction, and promotes sustainable engineering approaches aligned with real-world environmental and economic challenges.

Student roles:
The student will begin by reviewing literature on pyrolysis of plastic and biomass wastes to understand key technical and sustainability parameters. The main task will be to develop Aspen Plus models for pyrolysis systems, simulating different feedstock mixtures (e.g., polyethylene, lignocellulosic biomass) and process configurations. The student will conduct mass and energy balances, track key performance indicators, and generate outputs for LCA and TEA.
For the LCA component, the student will extract inventory data from the Aspen models and integrate it with environmental databases using software such as SimaPro or OpenLCA. This analysis will quantify the environmental impacts of pyrolysis compared to conventional waste disposal or energy production pathways.
In the TEA, the student will use data from simulations and literature to estimate capital and operational costs, develop economic models in Excel or Python, and conduct sensitivity analyses. Outputs will include net present value (NPV), payback period, and minimum fuel selling price.
The student will gain hands-on experience with process simulation, sustainability metrics, and economic modeling, contributing directly to research aimed at reducing plastic pollution and improving renewable energy solutions. Regular meetings with the research team will provide guidance and foster interdisciplinary discussion. Students will also be encouraged to document and present their work as part of their professional development.

Skills required:
This project is best suited for students from chemical engineering, environmental engineering, or related disciplines. Prior exposure to Aspen Plus or other process simulation tools is strongly preferred. Familiarity with basic LCA or TEA concepts is useful but not essential. Experience with Excel, Python, or MATLAB for data analysis is advantageous. The ideal candidate should be detail-oriented, proactive, and motivated to apply engineering tools to real-world sustainability challenges. A strong interest in modeling and waste-to-energy systems is essential.

Research area: Dr. Pivot's research area is in remote sensing technology and its applications in physical geography. Specifically, she specializes in monitoring and analyzing changes in Earth's surface processes, in particular snow cover dynamics and surface soil moisture and freeze-thaw state. She develops advanced observing systems, including drones, to acquire precise geospatial data more efficiently. Dr. Pivot also pioneers technology-driven solutions for online education, integrating fieldwork into courses through mobile technology-guided trips and virtual geographic environments.

Student roles:
Your role will primarily involve the following tasks:

1. Conducting a literature review to identify relevant methods and technologies related to smart GCPs, RTK-GNSS positioning, and drone mapping.

2. Reviewing the documentation of the selected RTK-GNSS receivers and associated hardware components to become familiar with their operation, configuration, and integration.

3. Assembling, configuring, and testing smart GCP prototypes incorporating RTK-GNSS receivers, microprocessors, and LoRa telemetry modules.

4. Designing and conducting field experiments to evaluate the performance of the smart GCP prototypes and their impact on mapping accuracy.

5. Evaluating the effects of target size, shape, color, number, and placement on georeferencing accuracy.

6. Preparing technical documentation, including a user guide for operating the smart GCP prototypes, and contributing to reports and scientific publications arising from the project.

Skills required:
Basic knowledge of RTK/PPK positioning systems is essential. Proficiency in programming (Python or C/C++) is required to interface GNSS receivers and telemetry modules with microprocessors. Experience with microprocessors (e.g., Raspberry Pi), Linux, GNSS technologies, remote sensing, photogrammetry, or drone mapping is an asset.

More importantly, you must demonstrate ingenuity, resourcefulness, and the ability to solve practical technical problems while working with individuals from diverse backgrounds. Experience with 3D printing is an asset but not required, as training can be provided during the internship.

Research area: I am interested in solid waste management. My recent projects at the Waste Management System Design Laboratory (WMSD Lab) focus on (i) waste generation and recycling behaviors during COVID-19, (ii) the use of remote sensing and satellite imagery in waste management applications, (iii) food waste and textile waste management, and (iv) low-level radioactive waste management. I have received awards in both teaching and research, and I am a Canada Research Chair (Tier 1) at the University of Regina. All my former Mitacs GRI students have enjoyed their internships with us. Check out our LinkedIn page.

Student roles:
The student should have an excellent background in Civil / Environmental engineering, with proper laboratory health and safety training. The student must be curious and inquisitive. He/she must be interested in sustainable solid waste management and be prepared to work with waste sampling and quantification. The ideal candidates should have excellent numerical modeling and laboratory skills. Effective communication skills (both oral and written) are expected. The student researcher should be able to work independently and collaboratively. Field experience with data collection, sampling, and materials characterization is an asset.

Waste Management System Design (WMSD) Laboratory at the University of Regina is partially funded by a Canada Foundation for Innovation John R. Evans Leaders Fund (CFI-JELF) grant and is a private research lab. The space is for trainees under the supervision of the Canada Research Chair (Tier 1) in Environmental Sustainability. Equity, Diversity, and Inclusion (EDI) is important to the success of my research work. A diverse workplace is a strength, and it requires awareness and sensitivity to cultural norms. Please be mindful of cultural differences.

Skills required:
Data will be collected from various sources, such as GeoHub, Geofabric, OpenStreetMap, Government of Canada's Census Subdivision Boundary Files. The project objectives are to (i) examine potential LLW and ILW disposal sites in Canada based on spatial accessibility and population densities, (ii) identify and rank suitable near-surface disposal sites for LLW and ILW using a data-driven GIS framework integrating multiple spatial layers. The Mitacs intern will be given opportunities to produce first-author publications. In fact, many of my former interns have produced their first publication with us.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 17 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with material characterization
training will be provided.

Skills required:
Mechanical/production engineering
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: The research area is digital and precision agriculture, in which methods of sensing and automation are applied on agricultural machines and indoor plant growing areas so as to optimize agricultural production and operations. The objectives include reducing labour, reducing the usage of chemicals, optimizing the usage of seeds and fertilizers, and increasing yield. The methodologies include developing sensors to collect data from machines, equipment, and their surrounding environment, developing methods to extract information from the data, and developing autonomous systems to control machines and equipment.

Student roles:
A student working in this project is expected to contribute in choosing suitable imaging, determining mechanical installation methods, and developing software to detect potato quality attributes

Skills required:
Programming skills using one of the following languages: C++, C#, or Python
Also, one of the following:
Machinery and mechanical design
Image processing
Statistics

Research area: My research focuses on advanced control and optimization methods that improve the reliability, safety, resilience, and real-time implementability of autonomous, high-precision, and safety-critical engineering systems. My work integrates model predictive control, fault-tolerant control, adaptive control, distributed control, and data-driven methods to address unexpected faults, uncertainties, constraints, and disturbances during system operation. My research has contributed to novel control frameworks that enable autonomous systems to maintain safe and reliable performance under degraded or abnormal operating conditions. Application areas include unmanned aerial vehicles, aerospace systems, multi-agent autonomous systems, adaptive optics systems, and precision mechatronic platforms.

Student roles:
The student will actively contribute to this project. The main responsibilities and deliverables include:

Background study and preparation:
> Conduct a focused literature review on UAV dynamics, model predictive control, fault detection, and fault-tolerant control.
> Summarize relevant methods, identify common actuator fault scenarios, and review how prediction-based or residual-based indicators are used in control systems.
> Learn the simplified UAV simulation model, MPC formulation, and example codes provided by the host research group.

Modeling and controller implementation:
> Develop or adapt a simplified UAV simulation model in MATLAB or Python.
> Implement a baseline model predictive controller for nominal trajectory tracking.
> Tune controller parameters and verify tracking performance under normal operating conditions.

Fault scenario design and proactive reconfiguration:
> Design representative actuator fault scenarios, such as partial loss of control effectiveness, input saturation, degraded actuator response, or disturbance-like fault effects.
> Analyze prediction-based early-warning indicators, including predicted tracking error growth, predicted constraint violation, input saturation, increased control effort, or mismatch between predicted and actual system behavior.
> Develop and test proactive fault accommodation or controller reconfiguration strategies triggered by these prediction-based indicators.

Evaluation, documentation, and communication:
> Compare the proactive strategy with a conventional reactive fault-tolerant control approach using tracking accuracy, control effort, constraint satisfaction, fault recovery time, and safety-related behavior.
> Prepare clear simulation plots, tables, and discussion of results.
> Document the code so that it can be reused by the research group.
> Prepare a final technical report and a short presentation summarizing the project motivation, methodology, simulation setup, results, limitations, and possible future extensions.
> Meet regularly with the supervisor, participate in research discussions with the host group, report progress, and revise the work based on feedback.

Skills required:
The student should have a background in engineering, robotics, aerospace, electrical engineering, mechanical engineering, computer engineering, or a related field. Basic knowledge of dynamic systems, control systems, and programming is expected. Experience with MATLAB or Python is highly desirable. Prior knowledge of optimization, UAVs, robotics, or model predictive control would be helpful, but is not required.

Research area: My research incorporates both experimental and computer modelling methods to investigate the range of scales from bone microstructure to whole-body musculoskeletal biomechanics, with a focus on the design of solutions for the prevention, care and treatment of diseased or injured systems. My testing and modelling of live human bone in organ culture is unique, made possible through a novel bioreactor and loading system. Through my industrial and clinical partners my research has direct impact through applications to product design (orthopaedic implants, medical devices, sports equipment), orthopaedic surgery, and therapy (pharmaceutical and physical).

Student roles:
The intern will be responsible for their own project defined as a sub project within from the main project (research objectives are to measure and simulate in explanted human trabecular bone). This project could be computer modelling and/or mechanical testing in a biosafety level 2 lab. The intern will be trained by my lab on computer simulation tools, mechanical testing equipment and biosafety lab protocols. The bone biomechanics subproject will be defined together with the student once they arrive, with the goal of a good alignment between the intern's interest and research topic. In addition to their own project, the student will be responsible for working with a current graduate student in working, also part of the main research project.

Skills required:
The skills we are looking for are ability to work in a team with good oral and written communication skills, and interesting learning about: 1. bone biomechanics; 2. computer modelling of bone; 3. mechanical testing of bone.

Research area: My research expertise spans several fields, including passive control of noise and vibrations, numerical modeling and simulation in vibroacoustics, robotics, artificial intelligence and production, and structure inspection. I explore ways to optimize the performance of mechanical systems using innovative techniques and by combining different disciplines. My objective is to contribute to creating sustainable and practical solutions to improve people's quality of life.

Student roles:
The intern will be responsible for designing and developing a robotic test bench that will measure the acoustic intensity radiated by a motor. To do this, they will need to control the robot to perform precise measurements by controlling the distance between the acoustic intensity probe and the motor, as well as the position of the probe. They will also need to integrate the acoustic intensity probe into the measurement system and ensure its proper functioning.

In addition, the intern will be tasked with developing a Statistical Energy Analysis (SEA) model of the system under study (the motor) using the VA One software. This SEA model will be used to analyze the source and propagation of acoustic waves in the motor system.

The intern will also need to be able to process and analyze the acoustic data collected using the robotic test bench, using tools such as MATLAB or Python.

Skills required:
We are looking for a student who is resourceful, independent, meticulous, creative, and has a good team spirit. It is also important for the student to have knowledge in design (Catia, SolidWorks, etc.), modeling (Simcenter 3D, Femap, VA One, Nova, etc.), programming (Matlab, Python, etc.), robotics (UR Robot), and artificial intelligence (data analysis).

Research area: Our research is focused on the transformation of biomass and waste into sustainable materials for environmental applications. We explore low-cost, eco-friendly strategies to treat contaminated water using advanced materials derived from natural sources. By combining principles of green chemistry and process engineering, we develop high-performance adsorbents for the removal of various pollutants-including heavy metals, dyes, and emerging contaminants. Core techniques in our lab include adsorption, electrosorption, and advanced oxidation processes. We aim to create scalable water treatment solutions that address global challenges in water quality, resource recovery, and environmental sustainability.

Student roles:
As a research intern, you will actively participate in the development of biochar adsorbents for water purification. Your responsibilities will begin with preparing biochar using microwave-assisted pyrolysis. You’ll explore the effects of different activation parameters, such as acid concentration, microwave power, and heating time, on the structural and chemical properties of the resulting material.
Once materials are prepared, you’ll conduct laboratory-scale adsorption experiments to evaluate their efficiency in removing contaminants from water. These studies will explore how factors like pH, ionic strength, and temperature influence adsorption behavior. You’ll collect and analyze data to understand the kinetics and equilibrium of the adsorption process, applying common models such as pseudo-first-order, pseudo-second-order, and Langmuir or Freundlich isotherms.
In addition to hands-on lab work, you’ll help characterize the materials using standard analytical methods to assess surface area, porosity, and functional groups. You will also be involved in interpreting results, comparing findings with the literature, and potentially contributing to research presentations or reports.
Throughout the internship, you’ll work closely with graduate students and receive training on safe lab practices, experimental design, and scientific communication. This role is ideal for students interested in sustainable technologies, environmental chemistry, or advanced materials for real-world applications.

Skills required:
We welcome students with a background in chemical engineering, environmental engineering, chemistry, or a related field. Prior experience in laboratory work, such as solution preparation, titration, or filtration, is valuable. Proficiency in data handling using Microsoft Excel is required; familiarity with modeling tools (e.g., MATLAB or Aspen Adsorption) is an asset. The ideal candidate is curious, detail-oriented, and motivated to work independently while contributing to a collaborative research team focused on environmental solutions.

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:
During the internship, the student will act as a junior researcher, fully integrated into our lab's culture under the direct mentorship of a senior PhD student. Responsibilities include:
- Conducting a literature review on cybersickness and spatial audio.
- Implementing subtle spatial auditory stimuli within a Unity VR environment.
- Co-designing and executing a formal user study observing strict ethical protocols.
- Performing statistical analysis on the collected data.
- Assisting in drafting an academic publication or presentation.
- Participating in weekly group meetings to present progress and exchange ideas with peers.

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

Preferred: Experience or strong interest in spatial audio, acoustic signal processing, or VR/XR development. Exposure to HCI user studies or UX evaluation.

Willingness to Learn: Highly motivated to learn statistical data analysis, safely apply cybersickness research protocols, and assist in scientific writing under the close mentorship of a multidisciplinary research team.

Research area: My field of research is geomatics engineering. My projects focus specifically on mobile LiDAR systems, which provide large-scale, accurate and affordable 3D point clouds. They are used in applications such as driverless vehicles, land-use planning and, more recently, for the creation of digital twins city. This is a virtual representation of a city's physical assets (e.g. roads; buildings; vegetated areas; ...) throughout their lifecycle. My project research objectives mainly concern the semantic segmentation of point clouds, notably using artificial intelligence approaches, and 3D modeling.

Student roles:
The intern’s work will involve integrating and merging data from the various sensors mounted on the bots. It will also involve developing methods for georeferencing the bot in real time, enabling it to visually determine its location based on landmarks in the area and the distance traveled. The work will also involve merging images captured from different viewpoints and extracting contextual information useful to the bot for navigating the urban environment. A key challenge of the work will be optimizing and ensuring the efficiency of processing to enable the bots to perform processing tasks as they move. The work will involve up to three bots active simultaneously in the environment and a variety of behaviors in terms of movement (e.g., stopping at stop signs or traffic lights; crossing an intersection safely; avoiding objects on the road; passing a slower bot; …). All of this work will be carried out using Python programming and open-source libraries and code (OpenCV; scikit-learn; PyTorch; etc.). It will build upon the developments already made by other students and interns in the lab.

Skills required:
Good knowledge and skills in image processing or computer vision
Knowledge of machine learning
Knowledge of robotics
Very good knowledge and skills in Python programming
Good knowledge and skills of Linux environment
Knowledge in the following areas would be an asset: deep learning; deep neural networks; geomatics;
Resourcefulness, ability to solve technical problem and creativity
Good communication skills to technical and non-technical communities, both verbal and writing
Ability to work both individually and as a team member

Research area: My laboratory at the Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR) aims to understand the mechanisms underlying performance and development of pathologies in wheelchair mobility. We aim to reduce the development of secondary pathologies associated to wheelchair propulsion by developing highly technological tools and methods to study and prevent such pathology development. The proposed research project is related to the integration of virtual reality in wheelchair training on a multi-sensorial simulator.

Student roles:
The student will be responsible for the modelling and for the programmation of dynamic elements in the scenes and games. He/she may also make part of other projects on wheelchair simulators that will be undergoing at this period. The time dedicated to the main et other projects will be adapted following these factors:

- The student's own learning objectives for this internship;
- The student's background;
- The other team members' backgrounds;
- The state of the project at the beginning of the internship.

Skills required:
The selected student for this internship will have a background, skills or very high interest in 3D modelling (e.g., Blender) and/or virtual reality (we use Godot, but experience with Unity or Unreal is very relevant). He/she must have a strong interest in multidisciplinary team research and be autonomous.

Research area: Experimental robotics, 3D perception and point cloud processing for two-arm robotic systems. The laboratory uses a robotic platform equipped with two Kinova Gen3 arms, with a software environment based on ROS2 and MoveIt2. The work focuses on the acquisition, processing and analysis of 3D data from depth sensors or simulation tools.

Student roles:
The intern’s role will be to study existing multi-view point cloud acquisition approaches, then to propose and test a protocol adapted to the laboratory context. He will have to acquire or use 3D data from objects placed in a controlled environment, implement a method for resetting and merging several views, then compare the results obtained with a single-view acquisition.

The work will include data preparation, implementation or adaptation of processing scripts, visualization of results and documentation of parameters used. The trainee will also need to identify observed limitations, e.g. effects of noise, occlusions, from the point of view or difficult to perceive surfaces.

At the end of the internship, he will have to provide a report, documented code, examples of point clouds and practical recommendations for reproducing the protocol.

Skills required:
The candidate should have a good grounding in Python or C++, as well as an interest in computer vision, 3D geometry and point cloud processing. Experience with ROS2, 3D sensors or 3D data visualization tools would be an asset. An ability to read scientific papers and clearly document one’s work is desired.

Research area: Newly developed "conventional" automobiles will either contain stop-start engines or will rely on hybrid technology to reduce fuel consumption. These new technologies present issues for conventional oils, which typically need to operate at higher temperatures than would be achieved when the combustion engine runs intermittently. Thus, lower viscosity oils must be developed to ensure the same lubrication and wear performance of the oil in the internal combustion engine. Nanoparticles are seen as one solution to this problem, as their addition to conventional and synthetic oils has been observed to have beneficial lubrication and wear properties.

Student roles:
The student will be expected to support graduate students in developing the nanoparticles, and coating the nanoparticles with two-dimensional lubricants. They will be expected to analyze the rheological properties of the lubricants and perform reciprocating friction testing of the lubricants using existing equipment in the lab. Students will also be required to perform metallurgical analysis of the worn materials, analyze friction and imaging data, as well as support the development of any publications that arise from this work.

Skills required:
The student should be highly motivated and interested in learning experimental techniques. The student is required to possess and improve upon a multidisciplinary skill-set. This project will require a background in computer programming, physics, materials science, and chemistry. No specific knowledge of the area of nanotribology or graphene science is required for this project, although insight into the area will be considered to be an advantage.

Research area: My research focuses on the application of electromagnetic imaging and spectroscopy for real-time quality monitoring of agri-food products. I develop and apply advanced data analytics techniques, including machine learning and artificial intelligence, to optimize processing time and extract meaningful patterns from large-scale agricultural datasets. My work also involves microstructural analysis of raw and processed agri-foods to better understand and enhance food quality and safety. Additionally, I investigate the use of physical treatments such as laser or LED biostimulation to improve seed viability and resilience.

Student roles:
As a student researcher on this project, you will contribute to the design, implementation, and analysis of a hyperspectral imaging-based approach for rapid, non-destructive prediction of germination rate in flaxseed and canola. You will begin by conducting a structured literature review on hyperspectral imaging, machine learning methods for seed quality assessment, and germination testing in Canadian oilseeds.

You will assist with sample preparation and spectral data collection using Vis-NIR and SWIR hyperspectral imaging systems. This will include scanning flaxseed and canola seed lots and carefully documenting sample information such as crop type, variety, storage condition, moisture content, and imaging settings. You will also assist with reference germination testing using standard protocols to generate ground-truth data for model calibration and validation.

A major part of your role will involve data analysis and model development. You will process hyperspectral images, apply spectral preprocessing methods, perform feature selection, and develop Gaussian Process Regression models for germination-rate prediction. You will also compare model performance with baseline approaches such as partial least squares regression and support vector machines. Where applicable, you will evaluate prediction uncertainty to assess the reliability of the models for practical seed quality screening.

You will maintain organized laboratory and data analysis records, follow safety and experimental protocols, and troubleshoot routine issues related to imaging, data processing, or modelling. You will communicate regularly with the principal investigator and research team, prepare progress updates, contribute to the final report, and assist with the preparation of conference presentations or manuscript drafts.

Skills required:
The ideal student should possess a strong background in agriculture, food science, biosystems engineering, or a related field. Familiarity with spectroscopic techniques and seed quality assessment would be an asset. Knowledge of chemometric or machine learning methods, including regression modelling and data preprocessing, is desirable. Proficiency in programming (preferably Python) for data analysis is expected. Effective verbal and written communication skills, along with the ability to work collaboratively within an interdisciplinary team, are essential for successful project completion.

Research area: Ingénierie analytique, chimiométrie Spectroscopie IR, Fluorescence, Raman Génie informatique

Student roles:
Participation au montage Arduino
Mise en œuvre du dispositif sur de vrais échantillons et collecte de données, mise en forme de la matrice de données,
Participation à l'amélioration de la qualité des données collectées (correction, essais de combinaison de capteurs variées)
Participation à la définition du cahier des charges techniques destiné à une entreprise de service pour la création d'un prototype fonctionnel compact sur circuit propriétaire

Skills required:
Connaissance de l'électronique de base, type ARDUINO,
Connaissance de la programmation Arduino sous Matlab ou sous Python
Goût pour le montage de circuit électronique simple et leur mise en œuvre pour la collecte de données réelles.

Research area: At TRIUMF - Canada's National Laboratory for Nuclear and Particle Physics we operate a unique Laser Ion Source to ionize short lived, radioactive isotopes for nuclear and particle physics experiments. Currently our research focuses on: (i) development of novel laser ionization schemes for different elements, using auto-ionizing atomic states, or Rydberg atoms, (ii) improved laser designs for e.g. higher power, higher stabiity. For the stydy of nuclear moments, atomic hyperfine structure and optical isotope shifts on low intensity beams we will augment laser resonance ionization with particle and nuclear decay identification.

Student roles:
Student is expected to learn the operation of experimental equipment and perform laboratory tasks after initial training independently. Will be working through a selection of supporting literature and textbook material, do literature research and give weekly presentations and progress reports on selected topics in atomic/molecular and optical physics topics related to the research performed. In parallel the student will experience work at an on-line isotope separator and accelerator facility. Supervised work with lasers and radiation areas may be required.

Skills required:
Interest in experimental work, techniques and instrumentation. Interest in operating experimental equipment.
Good documentation skills. A background in atomic/optical/nuclear physics and electronics or interest in these areas would be beneficial.
Student should be open to identify problems and be able to work independently and seek help from the group's PhD and MSc students as well as the group scientists as needed.

Research area: Dr. Taylor C. Steele is an Assistant Professor of Civil Engineering at the University of New Brunswick, where he leads a research program focused on a central question: how can we design buildings that recover quickly after an earthquake? His work brings together structural engineering, earthquake science, and practical design to develop “self centering” and “controlled rocking” systems. These systems allow buildings to move safely during shaking and return to position afterward with far less damage than traditional approaches.

Student roles:
The student will play an integral role in advancing a national‑scale research effort aimed at improving seismic design provisions for steel building systems (SBS) in Canada. With three years of civil engineering coursework completed, the student will apply and deepen their understanding of structural analysis, steel behaviour, and seismic design while contributing to both analytical and practical components of the project.

A primary responsibility will be assisting in the development of a database of SBS building geometries, material properties, and framing configurations. This will involve reviewing design drawings, extracting key parameters, and organizing data for subsequent analysis. The student will also support the creation and refinement of structural models—primarily using commercial software such as SAP2000, ETABS, or similar tools—to evaluate the dynamic characteristics of representative SBS frames. Under supervision, they will run modal analyses, interpret results, and compare predicted periods with those obtained from existing code equations.

The student will help conduct a literature review on period estimation methods, conventional construction provisions, and the seismic behaviour of rigid‑frame steel buildings. This work will inform the development of an improved empirical period equation tailored to SBS structures. Additional tasks may include preparing figures and tables, assisting with parametric studies, and helping validate analytical results against available experimental or industry data.

Throughout the project, the student will gain experience in structural modelling, data analysis, and technical communication. They will work closely with the research supervisor, participate in regular progress meetings, and contribute to interim reports or presentations. By the end of the project, the student will have developed practical skills in seismic analysis and steel design while contributing meaningfully to research with direct implications for future Canadian building codes.

Skills required:
The student should have a solid foundation in structural mechanics, basic steel design, and lateral load behaviour from their civil engineering coursework. They must be comfortable interpreting drawings, extracting geometric and material data, and working with structural analysis software or learning new tools quickly. Strong analytical skills, attention to detail, and organized data‑handling practices are essential. The student should also be able to interpret modal analysis results, contribute to literature reviews, and communicate findings clearly through figures, tables, and short technical summaries. Curiosity, initiative, and a willingness to engage with both modelling and research tasks are key.

Research area: The research area is digital and precision agriculture, in which methods of sensing and automation are applied on agricultural machines and indoor plant growing areas so as to optimize agricultural production and operations. The objectives include reducing labour, reducing the usage of chemicals, optimizing the usage of seeds and fertilizers, and increasing yield. The methodologies include developing sensors to collect data from machines, equipment, and their surrounding environment, developing methods to extract information from the data, and developing autonomous systems to control machines and equipment.

Student roles:
The students joining this project are expected to have the motivation to work in field, help in finding methods to mount cameras and sensors on machines, and collect data samples using spectral devices. At lab, the students will be helping in data analysis and developing machine learning algorithms

Skills required:
Two of the four following points:
-Programming skills using one of the following languages: C++, C#, or Python
-Electric and electronic circuits
-Plant nutrition
-Data analysis software such as excel and R

Research area: The Human-centered AI lab, led by Dr. Debasmita Mukherjee, focuses on developing AI solutions for human-focused applications. At the intersection of engineering, linguistics, and sociology, the research carried out in the lab aims to improve quality of life. The lab leverages explainable AI, generative AI and personalization applied to deep learning, to develop more trustworthy and ethical AI systems as well as adapt them to individual user preferences. Applications covered range from renewable energy, social AI assistants, to AI systems for medical data.

Student roles:
This project will involve:
1. Literature review and development of deep learning models based on diagnostic data for osteoporosis detection. Conduct data analysis to determine dataset imbalance, distribution of data, etc.
2. Development of explainable AI techniques applied to osteoporosis detection applying two broad types of algorithms: based on changing parts of the inputs to observe the effects on the outputs and algorithms based on reducing complexity of the model. Each student will be allocated one particular type of explainable AI algorithm.
3. Determine combination of explainable AI techniques to be used for generating explanations for two classes of users: “experts” who would prefer more details and “layperson” who would prefer less. This will lead to two explanatory setups for the two students.
4. Use prompt engineering to overlay over the two explanatory setups to enhance explanations into naturalistic language. This stage will be a team-work stage to validate and enhance explanatory frameworks.

Skills required:
The students (2) are required to possess the following skills:
1. programming in Python
2. deep learning for computer vision
3. ability to handle vision data
4. experience in utilising Hugging Face, GitHub, other code and dataset repositories

Research area: Je mène des recherches en électromagnétisme appliqué, principalement dans les domaines des antennes intelligentes, des systèmes RF et micro-ondes, des métamatériaux, des radars, des communications sans fil (5G/6G), et des applications biomédicales. Je m'intéresse aussi aux méthodes numériques pour la modélisation électromagnétique et dirige le Laboratoire de recherche sur les technologies RF avancées (LRTRA).

Student roles:
L’étudiant travaillera au sein d’une équipe de recherche pour optimiser et améliorer le système d’électroencéphalogramme sans fil existant. Il participera à l’analyse des données EEG, à l’amélioration des algorithmes de traitement du signal, et à l’intégration de nouvelles fonctionnalités, telles que la détection d’ondes cérébrales spécifiques. En collaboration avec les autres membres de l’équipe, il contribuera à l’optimisation des performances du système sans fil et à l'amélioration de la communication entre les capteurs et l'application. Le travail en groupe sera essentiel pour échanger des idées, valider les modifications et assurer l’intégration fluide des améliorations dans le système global.

Skills required:
Le candidat doit avoir une formation en génie biomédical, électronique ou un domaine connexe, avec des compétences en traitement du signal (notamment EEG) et en programmation (Python, MATLAB, C++). Une expérience avec des systèmes sans fil (Bluetooth, Wi-Fi) et des microcontrôleurs est souhaitée. Une familiarité avec les dispositifs EEG et l’analyse de données cérébrales, ainsi qu’une aptitude à optimiser des systèmes embarqués, est un atout.

Research area: Assistive technologies Prosthesis control Sensory feedback Adaptation Human-machine control framework Human-machine interaction

Student roles:
Student 1 will:
Conduct literature search
Review developed models
Assist with developing a mathematical model for human-machine interactions

Student 2 will:
Conduct literature search
Assist with data collection
Develop a data-driven model for human-machine interactions

Skills required:
Machine learning and reinforcement learning
Mathematical modelling and representation
Familiarity with computational methods is an asset
Use of data visualization tools such as python

Research area: My research expertise spans several fields, including passive control of noise and vibrations, numerical modeling and simulation in vibroacoustics, robotics, artificial intelligence and production, and structure inspection. I explore ways to optimize the performance of mechanical systems using innovative techniques and by combining different disciplines. My objective is to contribute to creating sustainable and practical solutions to improve people's quality of life.

Student roles:
The role of the student in this project will be to work on two main tasks. Firstly, they will need to optimize the performance of a CPP (Coverage Path Planning) trajectory planning algorithm, in order to ensure an optimal and complete path during data collection by a drone. They will be responsible for analyzing the existing algorithm and proposing improvements to optimize its efficiency.

Secondly, the student will need to design and optimize an autonomous algorithm capable of scanning and collecting real-time data, then classifying and detecting cracks on different structures. To do this, they will need to rely on knowledge of computer vision and image processing, as well as a deep understanding of the characteristics and properties of the different structures to be inspected.

Throughout the project, the student will need to work closely with the research team to share results and discuss implications for the practical application of crack detection algorithms. They will also need to document their work in detail and write regular reports on progress and results achieved.

Skills required:
We are looking for a student for an internship who should possess certain key skills to succeed in this project. We are looking for a resourceful and independent candidate who can work rigorously and creatively while having a good team spirit. The required skills for this internship include mastery of the following software and tools: PX4-Autopilot, MAVSDK, Gazebo, QGroundControl, and ROS. These tools will be essential to develop an autonomous system capable of analyzing pipeline images collected using drones.

Research area: My research focuses on Artificial Intelligence, Machine Learning, and Biomedical Signal Processing. I develop data-driven AI solutions that integrate multimodal sensing, temporal modeling, and physiological signal analysis for prediction, classification, and decision-making tasks in complex human and engineered systems. My work spans multimodal learning, multitask learning, affective computing, signal processing, and representation learning, with applications in healthcare, neuroengineering, and intelligent sensing technologies. Keywords: Artificial Intelligence, Machine Learning, Multimodal Learning, Biosignals, Physiological Signal Analysis, Affective Computing, Neuroengineering, Signal Processing, Human-Machine Interaction, Intelligent Sensing

Student roles:
The student will contribute to the development and evaluation of artificial intelligence models for EEG-based seizure detection. Responsibilities will include signal preprocessing, implementation of foundation models, development of parameter-efficient adaptation techniques, experimental evaluation, and performance analysis. The student will investigate methods for personalizing pretrained models to new patients while minimizing computational requirements. The student will also assist with result interpretation, technical reports, and scientific publications.

Skills required:
Must have at least one of the following skills:
1. Machine Learning / Deep Learning
2. Time-Series Analysis
3. Signal Processing
4. Parameter efficient fine tuning approaches

Research area: My research specializes in environmental fluid mechanics, using direct numerical simulations to study particle settling and dispersion in stratified shear flows. I focus on how Kelvin-Helmholtz instabilities and turbulence alter particle dynamics, with direct applications to marine carbon dioxide removal.

Student roles:
The student will serve as a data analysis and visualization specialist within the research team, focusing on post-processing output from direct numerical simulations of stratified shear flows. Specifically, the student will conduct below tasks.
1. Process Lagrangian particle tracking data: extract and clean simulation outputs (particle positions, velocities, settling rates) using Python.
2. Quantify settling and dispersion statistics: compute particle settling velocities, vertical flux, concentration fields, and dispersion metrics (e.g., mean-square displacement) under varying stratification and shear conditions.
3. Visualize multi-physics interactions: create publication-ready figures (e.g., particle trajectories overlain on turbulence fields, probability density functions of settling velocities) to reveal how convective and shear instabilities modify particle behavior.
4. Interpret results collaboratively: work closely with a PhD student to relate statistical findings to underlying flow physics, challenging traditional settling models.
5. Contribute to scientific communication: assist in preparing a conference paper and presentation, ensuring figures and analyses support key conclusions about particle transport in stratified environments relevant to marine carbon dioxide removal.

Skills required:
The student should have foundational knowledge of fluid mechanics (e.g., boundary layers, buoyancy) and familiarity with differential equations. Proficiency in Python for data processing, visualization, and basic statistics is essential. Prior exposure to scientific computing (e.g., NumPy, Matplotlib) is highly recommended. An interest in turbulence, environmental fluid dynamics, or climate mitigation is desirable. No prior experience with direct numerical simulations or Lagrangian particle models is required, but strong analytical and problem-solving skills are important.

Research area: Dr. Saman Hassanzadeh Amin is an outstanding scholar who has made significant contributions to the operations research and supply chain management fields. He has published 69 articles in well-known peer-reviewed journals. His publications have received over 6,400 citations to date in Google Scholar. Dr. Amin’s research is externally supported by difference agencies such as NSERC and SSHRC. His research interests include Supply Chain Management, Operations Management, Logistics, Operations Research, Optimization, Data Science, Machine Learning, and Decision Support Systems.

Student roles:
The student will design and develop an integrated decision-support framework for partner selection and RL network optimization under the supervision of Dr. Amin. This includes formulating a multi-objective optimization model, developing the network structure, and implementing the model in GAMS. The student will analyze and interpret the results and engage in regular discussions with Dr. Amin and the research group. The results of the project are expected to be published in a journal paper.

Skills required:
This project focuses on decision support systems for partner selection, integrating Multi-Criteria Decision-Making (MCDM) methods with a multi-objective optimization model. Therefore, it is preferred that the student has a background in operations research, multi-criteria decision-making, and optimization. Prior coursework in decision analysis and mathematical modeling is highly beneficial. Strong English language skills are required for academic writing and research dissemination.

Research area: My research is centered on lot sizing problems, integrated operational problems, and the integration of Industry 4.0 technologies. Using operations research tools, such as benders decomposition and column generation, I strive to solve relevant operational problems by first capturing all aspects of a problem in a mathematical model and then deriving managerial insights from the results.

Student roles:
The student will be responsible for developing the computer code for all numerical experiments, including implementing the mathematical models. The choice of programming language will be left to the student’s discretion. The student will also be responsible for designing an experimental plan for the project. Analysis of the results is also expected. In terms of solvers to be used, CPLEX, Gurobi, and Hexaly will be preferred.

Skills required:
The student must be familiar with basic concepts of operations research, including mathematical modelling and solution methods. The student must be enrolled in an operations management, industrial engineering, or operations research program. Students in a related field are also encouraged to apply to this internship. The student should have fundamental programming skills in at least one programing language (there are no restrictions on the preferred programming language). Experience with commercial solvers is a plus (CPLEX, GUROBI, etc…).

Research area: I am interested in solid waste management. My recent projects at the Waste Management System Design Laboratory (WMSD Lab) focus on (i) waste generation and recycling behaviors during COVID-19, (ii) the use of remote sensing and satellite imagery in waste management applications, (iii) food waste and textile waste management, and (iv) low-level radioactive waste management. I have received awards in both teaching and research, and I am a Canada Research Chair (Tier 1) at the University of Regina. All my former Mitacs GRI students have enjoyed their internships with us. Please check out our LinkedIn page.

Student roles:
I am an award-winning teacher and researcher at the University of Regina, and I would like to provide you with training on the following research skills: to identify good research questions, to gather and verify good data, to generate hypotheses, to conduct laboratory work, to develop models and to provide theorems, to make predictions and conclusions, and to solve practical problems using engineering tools. Typically, the student researcher will work on the following during their 12-week stay with us: (i) to conduct an effective literature review, (ii) to conduct laboratory work with various technical and statistical analyses, and (iii) to prepare technical reports.

The student researcher will prepare weekly presentations in front of other Master and Doctoral students (the “Research Group Meetings”), and draft scientific papers. Outstanding candidates will be invited to serve as co-authors on technical publications. Scientific publishing will jump-start your professional career. In fact, many of my former Mitacs GRI students have published their first peer-reviewed journal manuscripts under my supervision. Please contact me for samples. Please check my website (http://uregina.ca/~ng224/) for more on my teaching philosophy statement and past projects. Contact me if you would like to join us in solving the world’s waste problem.

Waste Management System Design (WMSD) Laboratory at the University of Regina is partially funded by a Canada Foundation for Innovation John R. Evans Leaders Fund (CFI-JELF) grant and is a private research lab. The space is for trainees under the supervision of the Canada Research Chair (Tier 1) in Environmental Sustainability. Equity, Diversity, and Inclusion (EDI) is important to the success of my research work. A diverse workplace is a strength, and it requires awareness and sensitivity to cultural norms. Please be mindful of cultural differences.

Skills required:
The student should have an excellent background in Civil / Environmental engineering, with proper laboratory health and safety training. The student must be curious and inquisitive. He/she must be interested in sustainable solid waste management and be prepared to work with waste sampling and quantification. The ideal candidates should have excellent numerical modeling and laboratory skills. Effective communication skills (both oral and written) are expected. The student researcher should be able to work independently and collaboratively. Field experience with data collection, sampling, and materials characterization is an asset.

Research area: The research area is digital and precision agriculture, in which methods of sensing and automation are applied on agricultural machines to optimize agricultural production and operations. The objectives include reducing labour, reducing the usage of chemicals, optimizing the usage of seeds and fertilizers, and increasing yield. The methodologies include developing sensors to collect data from machines, equipment, and their surrounding environment, developing methods to extract information from the data, and developing autonomous systems to control machines and equipment.

Student roles:
Students in this project are expected to help the development team analyzing and building communication system between sensors and machine. Also they are expected to find optimum specs of the mechanical components and draw necessary mechanical parts.

Skills required:
Two of the following four skills:
-Programming skills using one of the following languages: C++, C#, or Python
-Machine communication system
-Mechanical drawing on AutoCAD, SolidWorks
-Hydraulic systems

Research area: My recent research interests are best described as computational optimization and their applications, modeling and numerical analysis, metaheuristic/ evolutionary computing/artificial intelligence algorithms and their applications in engineering. In particular, I am interested in applying these algorithms to various type of datasets and use data mining approaches (my recent proposal for discovery NSERC).

Student roles:
Student is expected to know some skills of programming like MatLab or R software and swarm intelligence algorithms (is able to learn it quickly if s/he does not know). Student will write many computer programs by using matlab or R software or Javaa and he will combine ANFIS with various algorithms such as particle swarm optimization, social-spider optimization, sine-cosine algorithm Ant Colony Optimization algorithm, Genetic Algorithm. Students will compare these algorithms with each other algorithms. Also, student will help in writing a report and graph as a research assistant. After I am going to propose a new swarm intelligence algorithm and show this proposed algorithm is outperforms other algorithms in the literature. Our target is to get this work published in referred journal and present it in a conference.

Skills required:
Students know Python or R or Matlab software. Knowing swarm intelligence algorithms and data mining will be asset.

Research area: Pr Blais expertise lies in the development, verification, and validation of high performance digital models for fluid mechanics, heat transfer, and complex multi-physical and multi-scale phenomena. His research interests are in computational fluid mechanics (CFD), reacting flows, granular and solid-fluid flows, topology optimization as well as high-performance computing on distributed high-performance architecture and on GPU. He is the core developer of Lethe, an opens source high-performance and high-order implicit CFD solver (https://github.com/lethe-cfd/lethe) based on the open source DEALII platform (www.dealii.org).

Student roles:
With guidance from the supervisor, the student will design two families of DEM simulations using the open source software Lethe. The first one will reproduce a calibration experiment for which experimental results can be reproduced locally. This procedure will be used to evaluate the model parameters. The student will design an automatic calibration methodology by coupling the simulation to a black box optimisation algorithm and will assess the robustness of this approach. The sensitivity of the calibration will be established by exploring other operating conditions of the calibration experiment and identifying the range of validity of the calibrations. The now calibrated model will be used to design the second family of simulations which will reproduce the granular flow close to the spreading blade in powder bed additive manufacturing. The influence of particle size distribution, addition of ultrafine particles and particle surface modification on the flow characteristic of powders and on the bed porosity will be evaluated by using the experimentally calibrated model to simulate different additive manufacturing scenarios.

Skills required:
The applicant should be curious, autonomous and should have a keen interest for simulation and modelling. The candidate should be familiar with classical mechanics (motion of rigid bodies) and numerical resolution of ordinary differential equations (ODEs). Some basic knowledge of the Linux command shell (bash) and some programming experience (C++, Python) will be considered as significant advantage when applying to this internship. Previous experience with the Discrete Element Method (DEM) is an asset but is not mandatory.

Research area: 3D simulation, computational geometry and synthetic data generation for robotics. The project concerns the creation of families of parametric objects, simulated scenes and 3D point clouds. The generated data can be used to test 3D perception or analysis methods in controlled and reproducible configurations.

Student roles:
The intern’s role will be to study existing tools for procedural generation and 3D simulation on IsaacSim, then to develop a generator adapted to families of objects and scenes that are easy to set up. He will have to define geometric parameters such as dimensions, proportions, axes, position, orientation and point of view, then generate controlled variations.

The work will include the generation of 3D objects and scenes, the export of synthetic point clouds, the creation of geometric metadata and the documentation of product formats. The trainee will also have to produce several examples of automatically generated configurations, in order to show the diversity and reproducibility of the generator.

At the end of the internship, he will have to provide a documented procedural generator, examples of scenes and objects, synthetic point clouds, metadata files and documentation allowing to generate new configurations.

Skills required:
The candidate should have a basic knowledge of Python or C++, 3D geometry, simulation, 3D modeling or point cloud processing. Experience with IsaacSim, PyBullet or ROS2 would be an asset. The ability to produce clear and documented code is desired.

Research area: My research field specializes in several areas, including passive control of noise and vibrations, numerical modelling and simulation in vibroacoustic, robotics, artificial intelligence, production, and structure inspection. I explore ways to optimize the performance of mechanical systems using innovative techniques and by combining different disciplines. My goal is to contribute to creating sustainable and practical solutions to improve people's quality of life..

Student roles:
The student will contribute to developing a representative numerical model of the transmission system of a helicopter within the framework of the CB_TPA_Hybrid method. Specifically, they will work on setting up the finite element model, using available experimental data to validate the model and evaluate its robustness. They will also work closely with the existing research team to analyze the results and propose improvements or adjustments to the model. Finally, the student will be expected to document their work and present their results clearly and concisely.

Skills required:
For this internship, we are looking for a student with skills in mechanical engineering, with experience in numerical modelling and vibration analysis. A good understanding of modal analysis techniques and finite element methods is required for this internship.

The student should also know Matlab, Simulink, Simcenter 3D, and VA-One simulation software. Experience in programming with Python would be an additional asset.

In addition, we are looking for a student who is rigorous, capable of working independently and as part of a team and has sufficient time to meet deadlines.

Research area: Dr. Domenico Santoro's research focuses on advanced water treatment technologies, particularly in disinfection, biosolids treatment, and resource recovery. He specializes in applying computational fluid dynamics (CFD) and multi-scale modeling to optimize water treatment processes, including advanced oxidation for wastewater reuse. Dr. Santoro leads international collaborations with academic and industrial partners to innovate in water treatment technologies. His work spans areas such as ozonation, biosolids processing, and the development of sustainable water treatment practices. He is also involved in the development of patents and optimization strategies for water treatment systems.

Student roles:
Analytical Chemistry (2 students):
The primary role for these students is to analyze wastewater samples before and after treatment to assess the removal of micropollutants and pathogens. Students will use advanced analytical techniques such as high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectrometry (GC-MS). They will also engage in non-targeted analysis to identify unknown contaminants in the wastewater. These students will be responsible for data acquisition, processing, and interpretation of results, ensuring accurate identification of emerging contaminants. They will work closely with other project members to evaluate the effectiveness of quaternary treatments, such as advanced oxidation processes (AOPs) and ozonation, in degrading these micropollutants. The interns will gain exposure to cutting-edge tools and methodologies used in environmental chemistry and analytical testing, contributing significantly to the project’s success in determining the efficacy of treatment processes.

Chemical/Environmental Engineering (1 student):
This student will be responsible for understanding and optimizing the quaternary treatment processes such as AOPs and UV disinfection in the wastewater treatment system. The role includes designing experiments, setting up pilot-scale reactors, and evaluating the efficiency of various treatment methods for removing micropollutants. The intern will also be involved in reactor design and process optimization, ensuring that treatment processes are operating at their highest efficiency. This role requires knowledge of chemical kinetics, reactor engineering, and water treatment technologies, as well as the ability to work with interdisciplinary teams to interpret results and propose improvements.

Physics/Applied Mathematics (1 student):
The intern in this role will develop mathematical models and simulations to predict the behavior of pollutants during the treatment processes. They will use MATLAB or Python to model chemical reactions, mass transfer, and the kinetics of pollutant degradation. This student will contribute to process optimization by simulating the effects of various parameters on treatment efficiency.

Skills required:
Analytical Chemistry (2 students): Proficiency in HPLC, LC-MS, and GC-MS for micropollutant analysis. Experience in non-targeted analysis of unknown contaminants.

Chemical/Environmental/Biological Engineering (1 student): Knowledge of AOPs, ozonation, and UV disinfection. Experience in pilot-scale wastewater treatment processes.

Physics/Applied Mathematics (1 student): Expertise in fluid mechanics, transport phenomena, and mathematical modeling of chemical processes. Proficiency in MATLAB or Python for simulations.

Research area: My specialized research area is optical wireless communication, visible light communication (VLC), and next-generation secure wireless systems. The research combines communication theory, signal processing, channel modelling, physical-layer security, and emerging quantum communication concepts to design reliable and secure optical links for future 6G indoor networks and smart environments.

Student roles:
The students will work under my supervision to conduct a focused literature review, develop basic simulation models, analyze results, and prepare technical documentation. The students will begin by reviewing selected papers on visible light communication, optical wireless channels, and quantum-secured or quantum-inspired communication concepts.

The students will then implement a simplified VLC system model using MATLAB, Python, or another suitable simulation tool. The simulation work may include modelling an indoor optical link, receiver noise, channel conditions, and selected security-related performance metrics. The students will evaluate system performance under a limited number of scenarios and generate plots to compare conventional VLC with quantum-enhanced or quantum-inspired approaches.

The students will meet with me regularly to discuss progress, receive feedback, and refine the simulation assumptions. By the end of the 12-week internship, the students are expected to prepare a short technical report summarizing the literature review, simulation model, results, and main findings. Depending on the quality of the outcomes, the work may also contribute to a future conference paper or research manuscript.

Skills required:
The students should have a background in engineering, computer science, physics, or a related field. Knowledge of communication systems, signal processing, probability, linear algebra, and programming is preferred. Experience with MATLAB, Python, or similar simulation tools would be an asset.

Research area: This project focuses on advanced modeling of non-stationary robotic systems using quantum-inspired methods. It lies at the intersection of robotics, smart monitoring, and data-driven modeling. The research explores how concepts inspired by quantum theory—such as superposition of hypotheses—can be used to represent multiple possible system states and uncertainties in dynamic environments. Applications include mobile robots and industrial robotic systems operating under changing conditions. The goal is to improve prediction, robustness, and adaptability of models used for monitoring and maintenance.

Student roles:
The student will contribute to the development and evaluation of modeling approaches for non-stationary robotic systems. The main tasks include reviewing relevant literature, implementing baseline models, and developing a simplified quantum-inspired modeling framework.

The student will work with simulation tools and/or experimental datasets to analyze system behavior under varying conditions. They will compare classical modeling approaches with quantum-inspired representations and evaluate their performance in terms of prediction accuracy and adaptability.

The student will also participate in regular meetings, document their work, and present results at the end of the internship. Depending on progress, the work may contribute to a scientific publication or future research projects.

Skills required:
Basic knowledge of programming (Python or MATLAB), modeling, and data analysis. Familiarity with robotics, control systems, or machine learning is an asset. The student should be motivated, able to work independently, and interested in interdisciplinary research combining physics-inspired modeling and engineering systems.

Research area: Je mène des recherches en électromagnétisme appliqué, principalement dans les domaines des antennes intelligentes, des systèmes RF et micro-ondes, des métamatériaux, des radars, des communications sans fil (5G/6G), et des applications biomédicales. Je m'intéresse aussi aux méthodes numériques pour la modélisation électromagnétique et dirige le Laboratoire de recherche sur les technologies RF avancées (LRTRA).

Student roles:
L’étudiant sera responsable de la collecte et de l’analyse des données expérimentales issues du radar Doppler, ainsi que de l’implémentation des algorithmes de traitement du signal (tels que la FFT et le filtrage) pour détecter et classifier les mouvements humains. Il comparera les résultats expérimentaux avec les simulations électromagnétiques réalisées à l’aide du logiciel développé dans le laboratoire, et participera à l’ajustement du système pour améliorer sa précision. Il travaillera en étroite collaboration avec l’équipe de recherche pour valider et optimiser les performances du système dans des scénarios réels.

Skills required:
Le candidat doit avoir une formation en génie électrique ou traitement du signal, avec des compétences en programmation Python et en analyse de signaux radar (FFT, filtrage). Une expérience en détection de mouvements ou en reconnaissance de gestes est un atout. Le candidat devra être capable de comparer les résultats expérimentaux avec les simulations électromagnétiques réalisées par un doctorant dans le laboratoire. Des qualités d’autonomie, d’analyse et de travail en équipe sont essentielles.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 17 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help developing the model and a case study
training will be provided.

Skills required:
Mechanical/production engineering
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: Our research group operates within the Communications and Microelectronic Integration Laboratory (LaCIME) in the Department of Electrical Engineering at the École de technologie supérieure (ÉTS) and collaborates with members of the Regroupement Stratégique en microsystème du Québec (ReSMiQ). We develop organic semiconductor electronic devices including organic electrochemcial transistors, organic field effect transistors, printed chemiresistive and electrochemical sensors, printed RFID chipless antenna sensors, and soft MEMS such as ionic liquid crystal elastomers, for flexible applications in sensing, woundcare, and neuromorphic circuits. We focus on hands-on and modeling work, including cleanroom microfabrication (lithography, depositions), additive manufacturing processes, and material analysis.

Student roles:
In this project, the student will play a key role in the development and implementation of a method for localizing a mm-wave chipless RFID tag. The project will begin with an extensive literature review of the underlying theoretical concepts, including Maxwell’s equations, radar cross-section analysis, and localization principles, to establish a strong foundation for the research. Following this stage, the student will design and conduct experiments to determine the position and orientation of the RFID tag. This phase will involve setting up the experimental platform, calibrating the measurement equipment, and collecting data using a Vector Network Analyzer (VNA). The student will then analyze the measured data using signal processing techniques and evaluate the localization performance by comparing the estimated tag positions with the corresponding measurements obtained during the experiments. In the later stages of the project, machine learning algorithms will be investigated to improve localization accuracy, robustness, and real-time performance. This work will include selecting suitable machine learning models, training them using the collected experimental data, and optimizing their performance through iterative testing and refinement.
Throughout the project, the student will document all methodologies, results, and findings, producing a comprehensive report that evaluates and compares different localization techniques and demonstrates the effectiveness of the proposed approach. Regular documentation and reporting will also support close collaboration with the project lead and research team.

Skills required:
The ideal candidate should have a background in electrical engineering, physics, computer engineering, or related discipline. The intern will have a solid background in signal processing and experience using MATLAB and/or Python for data processing, algorithm development, and system analysis. Knowledge of electromagnetics, radio frequency (RF) systems, and wireless communications would be beneficial for understanding and advancing mm-wave RFID localization techniques. Knowledge of localization algorithms, radar systems, MIMO techniques, or RF measurements would be considered an asset. Strong analytical, problem-solving, technical writing, teamwork, and communication skills are essential.

Research area: My primary research area is smart and digital agriculture. My lab develops state-of-the-art digital technologies to enhance the productivity and sustainability of Canadian crop production systems. These technologies include, but are not limited to, unmanned aerial systems (or drones), ground-based robotics, multimodal sensors, Internet-of-Things (IoTs), physics-based models, and artificial intelligence (AI) approaches.

Student roles:
1. Assist with field data collection. We have regular field work led by graduate students. The prospective student is expected to assist the graduate student in field work activities, such as drone data collection, plant sampling, etc.
2. Learn and preprocess multispectral image data collected by drones. Graduate students in my team will guide the prospective students on data processing.
3. Conduct simple statistical analysis to investigate N management practices on corn growth and yield production.
4. Summarize the results and prepare a scientific report. This report is due around the last week of the internship.

Skills required:
1. Fundamental understanding of agriculture and farming.
2. Be willing to conduct field work.
3. Remote sensing experience is preferred.
4. Be able to conduct simple data analysis (e.g., linear regression, machine learning, etc.).
5. Strong teamwork and interpersonal skills.
6. Scientific writing experience is preferred.

Research area: The University of Northern British Columbia (UNBC) in Prince George, Canada, seeks qualified candidates to advance the applications of Self-Tapping-Screws (STS) in Timber Engineering. The research aims to offer robust and practical design guidance for STS in Cross-Laminated-Timber, considering gravity and seismic loads. Heightened public awareness regarding carbon footprints has dramatically increased the demand for sustainable construction and has initiated resurgence in the use of wood in tall residential and non-residential buildings. Together with the development of new engineered wood products and advanced connectors, wood-based hybrid structures that combine wood with different materials represent tremendous potential.

Student roles:
The student will be involved in the sampling, fabricating, testing and analyzing of timber assemblies. This will involve selecting test samples from a larger batch of material, using hand and machine tools to cut wood specimens, fabricating the timber assemblies, using advanced materials test equipment to carry out experiments, and applying spreadsheet tools and statistical programs to analyse the results. While previous knowledge and experience in some of these areas is expected, the student will be given all necessary guidance to acquire the necessary skills to successfully contribute to the research project.

Skills required:
The applicants should study for a degree in either civil engineering, wood science, or mechanical engineering.
The candidates further need to meet following criteria:
• Strong interest in timber engineering;
• Willingness to work in a laboratory environment;
• Demonstrated proficiency in written and spoken English.

Research area: Wildfire risk assessment and management in Canada’s Wildland–Urban Interfaces (WUIs) is commonly guided by frameworks such as the National Guide for wildland-urban-interface fires and FireSmart programs. These approaches rely primarily on deterministic, checklist-based evaluations of structural vulnerability and surrounding hazard conditions. While effective for identifying risk factors and guiding mitigation actions, they do not explicitly account for parameter uncertainty and interdependencies arising from the combined influence of wildfire exposure and population vulnerabilities on ignition outcomes. The proposed research aims to address the foregoing drawbacks by developing a probabilistic framework using Bayesian Networks (BNs).

Student roles:
The student is expected to study the FireSmart guideline and translate it into a BN framework.

Skills required:
Familiarity with system safety and quantitative risk assessment techniques such as fault tree analysis, event tree analysis, Bayesian networks.

Research area: The Robotic Interaction, Perception and Learning (RIPL) Lab researches perception systems to enable mobile robots to navigate and interact with their environment. Our research area spans sensor fusion, Simultaneous Localization and Mapping (SLAM) and deep learning representations of tasks. Our applications are in agriculture robotics, environmental and industrial monitoring and service robotics.

Student roles:
The student will be primarily responsible for implementing the neural network in Python and designing the training regime for self supervised learning. This will be done in consultation and with the support of the PI and graduate students. The student will also collect data using the quadruped robot on different terrain types and analyze data to compare performance of the state estimator against different baselines. The student will work closely with other undergraduate and graduate students and, depending on the success of the project, may contribute to writing publications which make use their work. The student will also participate in regular group meetings and present their progress to other lab members. The student may also be expected to assist other students in their robot experiments. Training on operation of the legged robot and access to GPU computing will be provided.

All robot experiments will be conducted under the supervision of the PI or graduate students. This will also include field deployments of the legged robot to relevant locations such as local forests or construction sites. The student will be expected to adhere to all safety rules and precautions.

Skills required:
The student must have the following experience:
- Programming experience, ideally Python and/or C++.
- Software version control, e.g. GitHub.
- Completed courses in Linear Algebra, Mechanics and Probability.
Ideally, the student also has prior experience working with robots, deep learning and/or the following software: Linux, ROS/ROS2, PyTorch, TensorFlow.

Research area: Je mène des recherches en électromagnétisme appliqué, principalement dans les domaines des antennes intelligentes, des systèmes RF et micro-ondes, des métamatériaux, des radars, des communications sans fil (5G/6G), et des applications biomédicales. Je m'intéresse aussi aux méthodes numériques pour la modélisation électromagnétique et dirige le Laboratoire de recherche sur les technologies RF avancées (LRTRA).

Student roles:
L’étudiant participera à la conception, à la simulation et à la fabrication d’une antenne directive sur circuit imprimé avec cavité Fabry-Perot. Il utilisera le logiciel HFSS pour modéliser et optimiser l’antenne, en collaboration avec un doctorant. Il sera également impliqué dans la fabrication du prototype et dans les tests expérimentaux pour évaluer la performance de l’antenne en termes de gain, de directivité et de compacité. Le travail en groupe et l’interaction avec l’équipe de recherche seront essentiels pour mener à bien le projet.

Skills required:
Le candidat doit avoir une formation en génie électrique ou en télécommunications, avec des connaissances en conception d’antennes et en simulation électromagnétique (HFSS). Une expérience en conception de circuits imprimés (PCB) et en fabrication de prototypes d’antennes est un atout. La familiarité avec les cavités Fabry-Perot pour la miniaturisation des antennes est souhaitée. Le candidat doit être capable de travailler en équipe et de collaborer à la conception, la fabrication et les tests expérimentaux.

Research area: The Advanced Control and Intelligent Systems (ACIS) Laboratory focuses on cyber-physical systems, autonomous robotics, and automated industrial inspection. Our research spans multi-robot collaboration, digital twinning, and autonomous unmanned aerial vehicle (UAV) payloads. We harness 3D computer vision, machine learning, and embodied AI to transition robotic platforms from controlled environments to real-world, uncertain conditions.

Student roles:
The intern will take ownership of the software pipeline on the Clearpath Jackal platform. They will integrate the camera and LiDAR sensors via ROS, ensuring precise time-synchronization of the data streams. The student will then adapt an existing SLAM-based Gaussian Splatting framework to process this sensor data. Daily tasks will include running data-collection missions with the mobile robot, tuning algorithm parameters, and comparing the resulting 3D models against ground-truth data. The intern will collaborate with team members to integrate their findings into the lab's larger 3D reconstruction codebase.

Skills required:
Candidates must be pursuing a degree in computer science, robotics, or electrical engineering. Strong programming skills are essential. The student must have foundational knowledge of computer vision, 3D geometry, and SLAM algorithms. Experience operating mobile robotic platforms and working within a Linux/ROS environment is highly advantageous.

Research area: I am trained as a Geological Engineer and Hydrogeologist. I combine field research

Student roles:
The student will work under Dr. Ryan's supervision to:
1. Compile publicly available data - Retrieve long-term daily and sub-daily discharge records (where available) from Water Survey of Canada for key Bow River stations (e.g., Lake Louise, Banff). Supplement with temperature and snow water equivalent data to help interpret melt periods.
2. Clean and prepare the dataset - Screen for gaps, rating-curve changes, and regulation effects; standardize timestamps; and separate daily from sub-daily records. Retain only years with sufficient temporal resolution to detect diel variability and ensure unit consistency.
3. Identify melt-driven diel cycling - Detect periods of consistent 24-hour discharge oscillations using objective methods (e.g., autocorrelation, spectral analysis, or amplitude thresholds). Define start and end dates of the melt season for each year and station.
4. Estimate daily meltwater contributions - For identified periods, integrate the discharge hydrograph (area under the flow vs. time curve) to obtain daily volumes. Separate the diel (melt-driven) signal from storm and baseline flow (e.g., smoothing or baseflow separation) to estimate daily melt contributions.
5. Aggregate to seasonal and annual scales - Sum daily melt contributions over each season to calculate total annual melt-derived discharge (m³/year), and express as a fraction of total annual flow.
6. Assess trends in magnitude and timing - Apply statistical tests (e.g., Mann–Kendall, regression) to evaluate trends in total melt contribution, proportional contribution, and timing metrics (onset, peak, cessation, season length).
7. Interpret in hydrologic context - Compare results with climate drivers (e.g., temperature, SWE, glacier extent) and consider confounding influences such as regulation and groundwater contributions.

Skills required:
The student should be familiar with river hydrographs, meteorological data (e.g., precipitation, snow water equivalent, air temperature, etc.). They should have basic scientific programming, data cleaning, and statistical analysis skills commensurate with some undergraduate training.

The ideal candidate would be able to work well both as a team member and independently.

Research area: My research focuses on artificial intelligence, optimization, digital twins, and advanced decision-support systems for industrial operations. We develop AI-driven methods that combine machine learning, process simulation, and optimization to improve reliability, productivity, energy efficiency, and sustainability in energy-intensive industries such as pulp and paper, mining, steel, cement, and chemical processing. The work bridges predictive analytics and operational decision-making to support smarter maintenance and asset-management strategies.

Student roles:
The student will assist in developing simulation and optimization models for industrial maintenance planning. Activities include data analysis, implementation of machine-learning algorithms, integration of predictive models with process simulators, and evaluation of operational scenarios. The student will help assess the impact of maintenance decisions on key performance indicators such as production, energy efficiency, and equipment reliability. Results will be documented through technical reports, presentations, and potential scientific publications.

Skills required:
Students should have a background in engineering, computer science, applied mathematics, industrial engineering, or a related discipline. Experience in programming (Python, MATLAB, or similar), optimization, machine learning, or process modeling is desirable. Familiarity with statistics, simulation, or operations research is an asset. The ideal candidate is interested in applying AI and optimization techniques to solve real-world industrial challenges.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 20 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with project tasks
training will be provided.

Skills required:
Mechanical/production engineering /Mechatronics
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 17 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with material characterization
training will be provided.

Skills required:
Mechanical/production engineering
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: Machine learning, data synthesis, geospatial modelling, land use change forecasting, electric vehicle infrastructure planning

Student roles:
Under the joint supervision of the host professor and graduate students, the student will implement the forecasting pipeline across three phases. First, the student will be responsible for collecting and cleaning socioeconomic datasets for one target region. Next, the student will use a land use change simulator to translate the data into model inputs and generate a multi-year land use change projection. Finally, they will test various machine learning models to forecast land use change in an efficient manner. Project deliverables include a curated dataset, a working pipeline, comparative results, and potential publications.

Skills required:
The student is required to have strong Python programming skills and comfort working with structured and geospatial datasets. The student should understand machine learning fundamentals (regression, neural networks) and be comfortable operating libraries such as scikit-learn, PyTorch, or TensorFlow. Experience with GIS tools such as QGIS is a great asset. The student should have strong analytical thinking, self-motivation, and strong English writing and speaking skills.

Research area: Our research focuses on the design, dynamics, and control of small robotic systems inspired by biological locomotion. we investigate advanced mechanisms and system architectures that enable flying robots to perform complex movements, such as transitioning between flying, jumping, perching, or interacting with surfaces. Recent work includes developing lightweight robots capable of multimodal locomotion and efficient environmental interaction. By integrating principles from mechanics, control theory, and materials science, our group aims to expand the capabilities and versatility of micro aerial vehicles for applications in monitoring, sampling, and exploration in challenging environments.

Student roles:
Students will take a central role in the design, fabrication, and experimental testing of a spherical hopping robot prototype. The project is structured around three progressive tasks:

1. Elastic Shell Design and Bounce Characterization:
Students will design and fabricate candidate spherical shell prototypes using elastomeric materials and 3D-printed or machined components. Using instrumented drop tests onto rigid surfaces, they will characterize the coefficient of restitution and contact dynamics as a function of shell geometry, wall thickness, and material. The goal is to identify shell designs that maximize energy retention per bounce while maintaining structural robustness.

2. Internal Pre-Loading Mechanism Development:
Building on the shell characterization, students will design and integrate a compact internal linear actuator mechanism capable of pre-tensioning the shell prior to ground contact. This requires careful attention to timing (synchronizing actuation with impact), mechanical packaging within the spherical envelope, and mass budget constraints. Bench-top experiments will validate whether active energy injection meaningfully increases bounce height compared to the passive baseline.

3. Spin-Control Module and Directed Hopping Experiments:
Students will integrate a motorized internal rotor to impart controlled spin to the robot during the flight phase. Following the established superball impact model, they will experimentally map the relationship between pre-impact spin rate and post-impact horizontal velocity, and compare results against theoretical predictions. Combined tests will demonstrate directionally controlled hopping sequences.
By the end of the internship, students will have contributed to a working bench-top prototype demonstrating active energy injection and spin-steered bouncing. The results will directly inform the design of a second-generation autonomous spherical hopper. Students will gain hands-on experience in mechanism design, impact dynamics, embedded actuation, and experimental robotics.

Skills required:
Applicants should have a background in mechanical engineering, electrical engineering, or a closely related field. Hands-on experience with prototyping, CAD design, and fabrication (e.g., 3D printing, machining, or laser cutting) is highly valued. Familiarity with basic electronics, microcontrollers, and sensors is an asset. Some exposure to dynamics, mechanics of materials, or control systems is beneficial for understanding the robot's bounce and steering behavior. The ideal candidate is self-motivated, comfortable working in a laboratory environment, and enjoys tackling multidisciplinary challenges that span mechanism design, materials selection, and experimental testing. Curiosity and eagerness to learn are equally important.

Research area: Research Spine biomechanics. What make a healthy spine vs someone with low back pain from a mechanical perspective.

Student roles:
Will execute above project in my lab.

Skills required:
Biomechanics, Material sciences, solid mechanics.

Research area: Je mène des recherches en électromagnétisme appliqué, principalement dans les domaines des antennes intelligentes, des systèmes RF et micro-ondes, des métamatériaux, des radars, des communications sans fil (5G/6G), et des applications biomédicales. Je m'intéresse aussi aux méthodes numériques pour la modélisation électromagnétique et dirige le Laboratoire de recherche sur les technologies RF avancées (LRTRA).

Student roles:
L’étudiant assumera un rôle actif au sein d’une équipe de recherche dédiée au développement et à l’optimisation d’un système de localisation intérieure 3D utilisant la technologie Ultra Wideband (UWB). Il participera à la conception, à la programmation et aux tests du système, notamment en implémentant un filtre de Kalman en Python, en développant une interface graphique pour la visualisation en temps réel, et en assurant la communication entre les modules Arduino et Python via WebSocket. Le travail en groupe étant encouragé, les stagiaires se partageront les tâches selon leurs compétences respectives, tout en collaborant étroitement pour assurer l’intégration cohérente des différentes composantes du projet.

Skills required:
Le candidat recherché devrait avoir une solide formation en génie électrique, en informatique ou dans un domaine connexe. Il ou elle devra posséder de bonnes compétences en programmation, notamment en Python (filtrage de données, traitement de signaux, interface graphique) et en Arduino (programmation embarquée). Une connaissance pratique des communications série/WebSocket et du format JSON est souhaitée. Une expérience préalable avec les technologies de localisation (comme UWB), les capteurs ou les systèmes embarqués constitue un atout. Le candidat doit également faire preuve de rigueur, d’autonomie et d’un bon esprit d’analyse pour contribuer efficacement à l’amélioration et à la validation du système.

Research area: 3D perception, computer vision and point cloud processing for robotic environments. The experimental context concerns a two-arm robotic platform, Kinova Gen3, with 3D data that can come from depth sensors, simulated scenes or public datasets. The work focuses on the extraction and analysis of target objects in 3D scenes.

Student roles:
The intern’s role will be to study existing methods for detecting and segmenting objects in 3D point clouds, then to select one or more relevant approaches with the supervisor. He will have to prepare or use 3D scenes containing several objects, implement an extraction pipeline of the target object and test this pipeline under different conditions.

The work will include data preparation, possible removal of irrelevant elements from the scene, extraction of the target object, visualization of results and analysis of boundaries. In particular, the trainee will have to document difficult cases, such as nearby objects, occlusions, measurement noise or surfaces that are difficult to perceive.

At the end of the internship, he will have to provide a report, documented code, examples of segmentation and an analysis of success and failure cases.

Skills required:
The candidate should have a basic knowledge of Python or C++, computer vision and point cloud processing. Experience with Open3D, ROS2, PyTorch or 3D segmentation methods would be an asset. An ability to test existing methods, analyze their limits and document the results is desired.

Research area: My specialized research area is in hybrid renewable energy systems and smart energy technologies, with emphasis on sustainable and cost-effective solutions for remote and underserved communities. My work combines system modeling, simulation, optimization, and performance assessment of PV, wind, battery storage, and microgrid systems using HOMER Pro and MATLAB/Simulink.

Student roles:
The student will support the modeling, simulation, and analysis of a hybrid renewable energy system for a remote community using HOMER Pro and MATLAB/Simulink. Responsibilities will include literature review, data preparation, system modeling, performance comparison of alternative designs, result analysis, and documentation of findings through plots, tables, and short technical summaries. The student will also participate in regular progress meetings and contribute to the final report and presentation.

Skills required:
The student should be an upper-year undergraduate in electrical engineering, energy engineering, or a related field, with basic knowledge of renewable energy systems, electric circuits, or power systems. Familiarity with MATLAB/Simulink is preferred, while prior experience with HOMER Pro is helpful but not essential. The student should have good analytical, problem-solving, and technical communication skills, along with an interest in renewable energy, hybrid power systems, and simulation-based research.

Research area: Our research focuses on the design, dynamics, and control of small robotic systems inspired by biological locomotion. we investigate advanced mechanisms and system architectures that enable flying robots to perform complex movements, such as transitioning between flying, jumping, perching, or interacting with surfaces. Recent work includes developing lightweight robots capable of multimodal locomotion and efficient environmental interaction. By integrating principles from mechanics, control theory, and materials science, our group aims to expand the capabilities and versatility of micro aerial vehicles for applications in monitoring, sampling, and exploration in challenging environments.

Student roles:
Students will play a central role in the design, fabrication, and experimental validation of a Galilean cannon payload-launch system integrated with a thrust-assisted hopping robot. The project is structured around three progressive tasks:

1. Analytical Modeling and Payload Interface Design:
Students will first develop a collision dynamics model for the two-body Galilean cannon system, predicting launch velocity and height as a function of robot rebound velocity, mass ratio, and interface coefficient of restitution. Guided by these predictions, students will design and fabricate candidate payload interface structures, varying geometry, material compliance, and mass, to maximize energy transfer to the payload while maintaining reliable, repeatable coupling.
2. Drop-Test Characterization and Interface Optimization:
Students will conduct systematic drop-and-launch experiments using a passive (non-flying) version of the hopping robot and instrumented payloads. High-speed imaging and onboard inertial sensors will be used to measure rebound and payload launch velocities, validating and refining the analytical model. Iterative experiments will identify the interface designs that best approximate elastic collision conditions, maximizing payload launch height across a range of drop heights.
3. Integration with Thrust-Assisted Hopper and Directed Launch Demonstration:
Building on the characterization phase, students will integrate the optimized payload interface with the active Hopcopter platform. This includes modifying the control strategy to consistently achieve target rebound velocities and evaluating how thrust assistance can amplify effective launch energy beyond passive drop tests. Combined experiments will demonstrate repeatable payload launches from a freely hopping robot, including preliminary characterization of launch direction control.

By the end of the internship, students will have contributed to a working bench-top prototype demonstrating impulsive payload launch via the Galilean cannon mechanism on a mobile flying–hopping robot. Results will directly inform future development of autonomous, agile payload-deployment systems.

Skills required:
Applicants should have a background in mechanical engineering, electrical engineering, or a closely related field. Hands-on experience with prototyping, CAD design, and fabrication (e.g., 3D printing, machining, or laser cutting) is highly valued. Familiarity with basic electronics, microcontrollers, and sensors is an asset. Some exposure to dynamics, mechanics of materials, or control systems is beneficial for understanding the robot's bounce and steering behavior. The ideal candidate is self-motivated, comfortable working in a laboratory environment, and enjoys tackling multidisciplinary challenges that span mechanism design, materials selection, and experimental testing. Curiosity and eagerness to learn are equally important.

Research area: The applicant’s (Ibrahim Deiab) research expertise is in the area of machining, machinability, process modeling, automation, sustainable and additive manufacturing and CAD/CAM. The applicant has 17 years of experience in manufacturing, machinability, materials characterization which is the core subject of this proposal. Dr. Deiab’s experience in modeling machining processes, CAD/CAM and optimization.

Student roles:
Student will help with project tasks
training will be provided.

Skills required:
Mechanical/production engineering /Mechatronics
knowledge of Manufacturing processes and materials science
Knowledge of software packages like Matlab, solidworks, master CAM is a plus

Research area: Assistive technologies, Prosthesis control, Sensory feedback, Adaptation, Human-machine control framework, Human-machine interaction

Student roles:
Develop new scenes within a custom virtual prosthesis emulator platform using Unity Game Engine
Develop and streamline the communication between the various systems used to control and track users actions with a virtual environment
Develop a graphical user interface within the Virtual Prosthesis Emulator platform that adjusts the feedback components to the users
Develop an automated system that adapts feedback to users skill level
Assist with data collection from users

Skills required:
Familiar with Github
Programming using C# and\or Python
Fair understanding of human movement
Familiarity with Unity Game Engine is an asset

Research area: Marlène Sanjosé is a Professor at ÉTS since 2019, where she is a member of the TFT (ThermoFluid for Transport) and GRAM (Groupe de Recherche en Acoustique à Montréal) research groups. An expert in high-fidelity numerical simulations, unsteady flow analysis, and aeroacoustic modelling for turbomachines, her key contributions include simulations of airfoil self-noise, tip-gap flow, and low-speed fans, alongside advanced post-processing tools for large-scale simulation data. She also brings expertise in noise prediction tools for axial rotating machines and experimental methods for analyzing low-speed turbomachine flows and acoustics.

Student roles:
The student will be in charge of the propeller test bench design, assembly and operation, with the support of technical staff. The student, together with the professor and a PhD student, will be in charge of the experimental campaigns and their results analysis.

Skills required:
The intern must be interested in computer-aided design, in manufacturing and assembly of precision parts, and in experimental methods for aerodynamics. The candidate must be creative and meticulous. Knowledge of turbomachinery, acoustic signal processing, and programming would be an asset. The ideal candidate must demonstrate strong communication and task planning skills. The intern will be required to work collaboratively with various technical staff across multiple laboratories and must adhere to the lab operational and safety protocols.

Research area: Marlène Sanjosé is a Professor at ÉTS since 2019, where she is a member of the TFT (ThermoFluid for Transport) and GRAM (Groupe de Recherche en Acoustique à Montréal) research groups. An expert in high-fidelity numerical simulations, unsteady flow analysis, and aeroacoustic modelling for turbomachines, her key contributions include simulations of airfoil self-noise, tip-gap flow, and low-speed fans, alongside advanced post-processing tools for large-scale simulation data. She also brings expertise in noise prediction tools for axial rotating machines and experimental methods for analyzing low-speed turbomachine flows and acoustics.

Student roles:
The student will be in charge of the definition of the computational domain for the simulation of propellers. The student will prepare the computational grids, and validate the numerical setups with experiments. The student, together with the professor and a PhD student, will be in charge of the development of the acoustic propagation method.

Skills required:
The intern must be interested in numerical methods for fluid mechanics, in programming in Python language. The candidate must be creative and meticulous. Knowledge of turbomachinery and acoustic signal processing would be an asset. The ideal candidate must demonstrate strong communication skills as he will have to participate to regular group meeting and write CFD reports.

Research area: Materials characterization, Biocomposites processing, Wood valorization and processing

Student roles:
The intern will work closely with the research team and will be responsible for several tasks including:
Follow mandatory training in occupational health and safety and on the use of laboratory equipment;
Conducting a bibliographic study related to the research project;
Performing laboratory work on biocomposite shaping and characterization of their properties;
Collecting and processing experimental data and analyzing the results;
Assisting Ph.D. students and post-docs with experiments.
The writing of an internship report;
Presentation of the results to the research team and the project partners;
Participate in team meetings
Other tasks and responsibilities related to the project.

Skills required:
Education: Materials engineering, chemical engineering or related discipline
Experience in research and laboratory work
Good oral and written communication skills (French and/or English)
Ability to work in a team
Autonomy and leadership skills

Research area: Natural disasters, collaborative rescue, unmanned aerial vehicles (UAVs), electric vehicles (EVs), reinforcement learning

Student roles:
The student will contribute to all three stages of the project under the supervision of the host professor. In the first stage, the student will be familiar with diffusion-model-augmented disaster data to generate a diverse set of disaster-impacted scenarios, including data analysis and scenario validation. In the second stage, the student will help design and implement a time-iterated matching algorithm that pairs available EVs and UAVs to rescue tasks, exploring both centralized and decentralized formulations and analyzing their trade-offs in efficiency, cost, and scalability. In the third stage, the student will evaluate the proposed algorithm against benchmarks.

Skills required:
The student should have a solid programming background, preferably in Python, and a working knowledge of machine learning and deep learning. Familiarity with generative models (e.g., diffusion models, GANs, VAEs) is a strong asset, as is experience with optimization, algorithm design, or combinatorial matching problems. A sound foundation in probability, statistics, and linear algebra is expected. Prior exposure to electric vehicles, UAVs, transportation networks, or disaster-resilience modeling is advantageous but not required. The ideal candidate is self-motivated and comfortable working independently and collaboratively in a research setting, with strong written and verbal English communication skills.

Research area: My research focuses on Artificial Intelligence (AI) safety, reliability, risk assessment, cybersecurity, and human-AI interaction in engineering systems. The goal is to understand how technical, operational, human, and security-related factors influence AI performance and decision-making in applications such as industrial automation, energy systems, manufacturing, and transportation. This research supports the development of approaches to identify and assess AI-related risks and to improve confidence in the safe, reliable, and responsible use of AI technologies in engineering environments.

Student roles:
The student will investigate real-world challenges associated with the deployment of Artificial Intelligence (AI) in engineering systems. By examining current research, published AI incidents, and case studies, the student will identify and classify factors that influence AI safety, reliability, and trustworthiness, including data quality, model uncertainty, explainability, cybersecurity vulnerabilities, human oversight, operational conditions, and regulatory considerations.
Using information gathered from the literature and case studies, the student will contribute to developing a structured taxonomy of AI risk factors and help create a preliminary framework for organizing and assessing AI-related risks in engineering systems. The student will also create figures, tables, and visual summaries to communicate research findings and compare existing AI governance and regulatory approaches relevant to engineering applications.
Throughout the internship, the student will participate in regular research meetings, discuss findings with the research supervisor, and receive guidance on research methods, technical communication, and scientific writing. The student will prepare a final research poster and a technical report summarizing the project's results.

Expected Outcomes
• Literature review on factors influencing AI safety and reliability.
• Analysis of published AI incidents, failures, and operational challenges.
• Taxonomy of technical, human, operational, cybersecurity, and regulatory risk factors.
• Preliminary framework for organizing AI-related risks in engineering systems.
• Comparative overview of selected AI governance and regulatory approaches.
• Research poster and final technical report.
• Exposure to current approaches in AI governance, risk management, and responsible AI deployment.
• Potential contribution to future research publications and conference presentations.

Skills required:
The ideal candidate has a background in Engineering, Computer Science, Data Science, Information Systems, Cybersecurity, Applied Mathematics, or a related discipline. An interest in Artificial Intelligence, engineering systems, safety, risk analysis, cybersecurity, or human-centered technology is desirable. Strong analytical thinking, communication skills, and a willingness to learn are important. Previous research experience is beneficial but not required. Familiarity with programming, data analysis, or technical writing would be an asset.

Research area: The Dynamics and Digital Manufacturing (DDM) lab of UVic specializes in machine tool dynamics, intelligent manufacturing, and mechanical vibrations. Our lab is equipped with various research test setups for advanced manufacturing, including a 6-axis KUKA robot with a machining end-effector, a 3-axis Computer Numerical Control (CNC) machine tool, a micro-milling center, dSpace real-time computer, a full suit of force and vibration measurement sensors and actuators.

Student roles:
You will work closely with mechanical engineering graduate students to implement their new control methods in the 3D printer’s open-source firmware. In addition, you will have access to our research equipment to experiment and learn about your topics of interest in dynamics, vibrations, and manufacturing. You will attend our regular group meetings to learn about other team members' research projects, present your work, and receive feedback from your lab mates. We are deeply committed to fostering a collegial, inclusive, and equitable lab environment, ensuring everyone feels valued and supported.

Skills required:
The ideal candidate is a senior software or mechatronics engineering student with the following skills
- Experienced in embedded systems and firmware development. This includes writing code for firmware, understanding hardware interfaces, and debugging firmware issues.
- Proficient in C++ programming
- Familiar with the principles of 3D printing technology. Knowledge of slicing software and G-code is an asset.
- Basic knowledge about sensors, actuators, and motor control.
- The student should be able to troubleshoot firmware, hardware, and software issues.
- Excellent oral and written communication and teamwork skills

Research area: Solid mechanics Structural engineering Dynamics and vibration

Student roles:
Setup the test
Conduct the test
Collect data and process data
Reveal results and conclusions

Skills required:
Solid mechanics
Finite element method

Research area: The Advanced Control and Intelligent Systems (ACIS) Laboratory focuses on cyber-physical systems, autonomous robotics, and automated industrial inspection. Our research spans multi-robot collaboration, digital twinning, and autonomous unmanned aerial vehicle (UAV) payloads. We harness 3D computer vision, machine learning, and embodied AI to transition robotic platforms from controlled environments to real-world, uncertain conditions.

Student roles:
The student will collaborate closely with graduate students and researchers in the robot cell. Their primary role will involve designing and executing data-collection protocols. They will physically operate the robotic arms (via teleoperation or scripting) and the XR headsets to perform industrial tasks, capturing the necessary synchronized data streams. Once the dataset is established, the intern will deploy open-source VLA models in our testing environment, run benchmarking scripts, and analyze the models' failure rates and operational latency. The student will document the dataset structure and present their benchmarking results to the lab.

Skills required:
Applicants should be studying computer science, engineering, or a related field with a strong interest in machine learning and robotics. Proficiency in Python and experience with machine learning frameworks (e.g., PyTorch) is required. Familiarity with ROS2, Gazebo, or Isaac Sim, as well as an understanding of robotic kinematics or digital twinning, is highly preferred.

Research area: Visuo-inertial navigation, nadir view, UAV, sim2real transfer, visual odometry, embedded systems

Student roles:
The student will be responsible for porting and adapting the visual-inertial navigation code developed by the team. They will then carry out test and validation campaigns in simulation in order to characterize the system's limits and performance under various operational conditions.
The student will also contribute to the integration of the system onto the embedded hardware, as well as to the mechanical assembly of the fixed-wing drone prototype. Throughout the internship, the student will document their methods and results to support the project's conclusions and the subsequent in-flight validation phases.

Skills required:
Strong background in C/C++ programming (with embedded experience) and Python, familiarity with ROS/ROS2, experience in the design and mechanical assembly of drone platforms, and ease working across software development, simulation, and hardware integration.

Research area: My current research interests include: • Artificial intelligence (AI) for wireless networks • 5G and 6G enabling technologies • Reconfigurable intelligent surfaces and smart environments • Physical layer security in wireless networks • Optical wireless communications • Machine learning for wireless resource management and optimization • Hybrid optical wireless and radio frequency communications • Underwater communications

Student roles:
The student will work under my supervision in collaboration with Ph.D students and Master students, and will participate in algorithm development and simulation.

Skills required:
Required background and skills:
Optical wireless communications systems, specifically free-space optical and visible light communication
Simulation of optical components
Good programming skills

Research area: Nos travaux de recherche s’inscrivent dans le domaine de l’environnement minier, plus particulièrement dans l’évaluation de la performance des systèmes de recouvrement imperméables intégrant des géomembranes. Ces systèmes sont conçus pour limiter les flux d’eau et d’oxygène, contribuant ainsi au contrôle de la génération de drainage minier acide (DMA). Cependant, les géomembranes sont des matériaux synthétiques qui se dégradent dans le temps. Dans ce contexte, une partie de nos travaux vise à évaluer la durabilité de ces géomembranes utilisées dans les systèmes de recouvrement minier.

Student roles:
Au cours du stage, la personne étudiante réalisera les différentes caractérisations au laboratoire. Il s’agit d’effectuer différents essais de caractérisation sur les géomembranes des cellules expérimentales :
• Caractérisation chimique : mesure du temps d’induction oxydative standard (ASTM D3895) et à haute pression (ASTM D5885), détermination de l’indice carbonyle par spectrométrie IR (ASTM E1252; ASTM E168)
• Caractérisation mécanique : essai de traction simple (ASTM D6693) et en bande large (ASTM D4885)
• Caractérisation hydrogéologique : essai de perméabilité (NF EN 14150) et de sorption/diffusion d’oxygène.
La personne étudiante sera également responsable de la gestion, du traitement et de l’analyse des données issues des essais de caractérisations.
Enfin, la personne étudiante sera amenée à documenter ses travaux, à présenter régulièrement l’avancement de ses résultats et à contribuer à la rédaction de rapports techniques ou scientifiques.

Skills required:
La personne candidate doit posséder un intérêt marqué pour le domaine minier, avec une sensibilité particulière aux enjeux environnementaux et à la restauration des sites miniers. Elle doit démontrer de solides aptitudes en science des matériaux et en géotechnique. Une bonne capacité de synthèse et de rigueur scientifique est essentielle. Des connaissances en chimie des polymères et de l’expérience dans des travaux de laboratoire constituent un atout important.