Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 620 projets complétés

2978
AB
5221
C.-B.
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projets par catégorie

Characterization of coinfections with atypical and typical variants of Melissococcus plutonius

In this project, we will investigate the strain dynamics and antimicrobial sensitivity of the bacterial pathogen of honey bees, Melissococcus plutonius, which causes European foulbrood disease of honey bees.

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Superviseur du corps professoral :

Sarah Wood

Étudiant :

Partenaire :

Cornell University

Discipline :

Life Sciences

Secteur :

Agriculture and Food

Université :

University of Saskatchewan

Programme :

Globalink Research Award

From Smart Water Narratives to Operational Reality: Digitization and Urban Water Service Delivery in India

This study examines how digital tools, such as household water meters, sensors, and monitoring dashboards, are being used in urban water utilities, and whether they are actually improving everyday service delivery. Focusing on medium-sized cities in India and drawing comparative insights from Canada, the study looks beyond technology installation to understand how governance arrangements, staffing and institutional capacity, infrastructure conditions, and day-to-day operating practices shape what these digital systems can (and cannot) achieve. The project will generate practical insights for utilities and policymakers on when and how digitization can improve accountability, service quality, and public trust. Participating institutions will benefit from collaborative research, comparative learning across India and Canada, and evidence that can support more effective, context-specific approaches to digital water management that are adaptable and scalable.

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Superviseur du corps professoral :

Nidhi Subramanyam

Étudiant :

Partenaire :

Indian Institute of Technology Bombay

Discipline :

Sociology

Secteur :

Education

Université :

University of Toronto

Programme :

Globalink Research Award

Vancouver Heritage Shift for a Critical Cultural Landscapes Understanding of Urban Planning History and Future

The intern will assist Heritage Vancouver Society in documenting and evidencing a cultural landscapes based approach to urban heritage planning, consultation and advocacy, using site-based archival research, urban sketching, and the production of companion digital stories documenting sites of cultural heritage value in the City of Vancouver. The research and site visual documentation will be paired with other media and engagement activities to lead to web publication of a series of interpretive digital stories prepared for public exhibition and use by educational and heritage institutions.

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Superviseur du corps professoral :

Meg Holden

Étudiant :

Partenaire :

Heritage Vancouver Society

Discipline :

Sociology

Secteur :

Arts, entertainment and recreation

Université :

Simon Fraser University

Programme :

Business Strategy Internship

L2M – A novel bioresorbable stent design for targeted support and regeneration of coronary artery vessels post myocardial infarction

The goal of this project is to create a next-generation biodegradable coronary stent for people treated after a heart attack. It will provide strong but temporary support to keep the artery open while it heals, then gradually dissolve over time to prevent a long-term implant. It also has a surface coating to speed the formation of a healthy inner lining of the artery, which reduces the risk of clotting and minimizes re-narrowing. The design also targets difficult calcified arteries where current stents can heal poorly and fail late.

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Superviseur du corps professoral :

Tohid Didar

Étudiant :

Partenaire :

Innovation Factory

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

McMaster University

Programme :

Business Strategy Internship

L2M – Rapid Fabrication of Cell Sheets for Advanced Burn Care

This project will help reduce the market and adoption risk for our rapid-manufacture, bioactive dermal regeneration cell-sheet technology for complex wounds such as extensive full-thickness burns. Over four months, the intern will speak with key stakeholder to confirm the most important unmet needs, identify the best first clinical use case, and clarify what proof and documentation hospitals would need before they can approve and buy the product. The intern will also translate the product’s technical and preclinical advantages into a clear value story that matters to decision-makers. The partner organization will benefit by receiving an evidence-based business model and a clear, practical summary of what the market needs, what barriers must be addressed for adoption, and what the next steps should be to move the technology toward real-world clinical use.

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Superviseur du corps professoral :

Ravi Selvaganapathy

Étudiant :

Partenaire :

Innovation Factory

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

McMaster University

Programme :

Business Strategy Internship

Group theory of interferometric amplitudes in the limit of very many particles

Partial distinguishability is now recognized as important in quantum technologies because it directly controls the structure of multi-particle quantum interference, which underpins the advantage of many photonic platforms for computing, sensing, and communication. In realistic devices, photons (or other identical particles) are never perfectly identical: residual differences in time of arrival, spectrum, polarization, or spatial mode introduce partial distinguishability, turning ideal coherent interference into a mixture of interfering and non-interfering processes. This affects gate fidelities in linear-optical quantum computing, limits precision in interferometric metrology, and determines whether tasks such as boson sampling remain classically intractable. Crucially, recent work has shown that partial distinguishability is not merely a source of experimental “noise” but a quantifiable resource parameter that can be modelled, measured, and sometimes mitigated or even exploited. This project is about understanding and controlling partial distinguishability so it can be used for applications such as benchmarking quantum advantage, designing scalable photon sources and interferometers, and defining realistic performance thresholds for near-term quantum technologies.

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Superviseur du corps professoral :

Hubert de Guise

Étudiant :

Partenaire :

Universidad de los Andes

Discipline :

Physics

Secteur :

Quantum Science

Université :

Lakehead University

Programme :

Globalink Research Award

Prédiction spatiale de l’indice de biodiversité potentielle en forêt boréale mixte par télédétection

Les vieilles forêts jouent un rôle central dans le maintien de la biodiversité et des fonctions écologiques forestières, mais leur prise en compte dans l’aménagement forestier demeure limitée par des outils de diagnostic reposant principalement sur des seuils d’âge et des proportions de forêts résiduelles. Cette approche ne permet pas d’évaluer adéquatement la qualité écologique des peuplements, qui continuent d’évoluer bien au-delà des seuils actuellement utilisés. À ces limites s’ajoutent des contraintes opérationnelles majeures, l’étendue des territoires forestiers québécois rendant difficile la réalisation d’inventaires écologiques détaillés à grande échelle. Dans ce contexte, l’Indice de Biodiversité Potentielle (IBP), développé en Europe, constitue une approche prometteuse pour une évaluation rapide et standardisée de la biodiversité forestière à partir d’attributs structuraux, compositionnels et environnementaux. Toutefois, cet indicateur doit être adapté aux forêts boréales mixtes et appuyé par des outils de prédiction spatiale. Ce projet vise à adapter l’IBP au contexte boréal québécois et à développer des modèles prédictifs fondés sur des données LiDAR aéroportées afin d’estimer la biodiversité potentielle à grande échelle. En combinant inventaires terrain et télédétection, il ambitionne de fournir de nouveaux outils opérationnels pour améliorer l’évaluation et la conservation des vieilles forêts dans une perspective d’aménagement forestier durable.

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Superviseur du corps professoral :

Maxence Martin

Étudiant :

Partenaire :

Université Grenoble Alpes

Discipline :

Life Sciences

Secteur :

Education

Université :

Université du Québec en Abitibi-Témiscamingue

Programme :

Globalink Research Award

Hardware/Software Co-Design and FPGA Prototyping of NIST Post-Quantum Cryptography (PQC) Algorithms

This project explores how post-quantum cryptography (PQC) algorithms can be efficiently implemented on hardware platforms to prepare for future quantum-safe security needs. As new NIST-standardized PQC algorithms replace older encryption methods that are vulnerable to quantum attacks, designers face challenges due to higher computation and memory demands. The project will study a representative PQC algorithm and build an FPGA prototype that combines software and hardware acceleration for its most performance-critical components. By comparing software-only implementations with hardware designs created using both high-level synthesis and traditional hardware design methods, the project will identify practical trade-offs in speed, resource usage, and design effort. Participating institutions benefit from reusable FPGA prototypes, clear experimental data, and concrete design guidance that can support future research, education, and real-world deployment of quantum-resistant security in embedded and edge computing systems.

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Superviseur du corps professoral :

Felipe Gohring de Magalhaes

Étudiant :

Partenaire :

Institut-Mines-Télécom Atlantique

Discipline :

Computer science

Secteur :

Cyber Security; Quantum Science

Université :

Polytechnique Montréal

Programme :

Globalink Research Award

Modeling of Electrochemical Hydrogen Compression

This project aims to improve the performance of electrochemical hydrogen compressors (EHCs), which are used to recover and compress hydrogen for clean energy systems such as fuel cells. During operation, some hydrogen is lost, increasing cost and reducing efficiency. The performance of EHCs depends strongly on how hydrogen and water move through the polymer membrane at very small scales. This project addresses this limitation by developing detailed computer models that represent the membrane’s internal pore structure and simulate hydrogen and water transport under realistic conditions. Using computational techniques, the project will study how different properties affect hydrogen transport and efficiency losses. The simulation results will be compared with experimental data from the host institution to ensure accuracy and practical relevance. The findings will help identify better membrane designs and operating strategies.
The project will benefit both institutions by combining complementary expertise. The home institution at York will gain new modeling tools and computational data for ongoing research in fuel cells and energy systems, while the host institution at DHBW Mannheim will benefit from advanced modeling to better interpret experimental results. The collaboration will strengthen research ties, support joint publications, and help train highly qualified researchers in hydrogen technologies.

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Superviseur du corps professoral :

Cuiying Jian

Étudiant :

Partenaire :

DHBW Mannheim

Discipline :

Engineering

Secteur :

Energy and Utilities

Université :

York University

Programme :

Globalink Research Award

L2M-The development of eco-friendly and renewable bioplastic materials from agricultural and food waste streams

This project will help BioPolatis turn their 100% compostable bioplastics into a market-ready product. The intern will research potential customers, test business ideas, and develop a clear commercialization strategy. By the end of the project, BioPolatis will have actionable steps to grow their business, scale production, and enable companies to adopt sustainable packaging solutions. This supports Innovation Factory’s mission by accelerating innovation, fostering entrepreneurship, and helping bring environmentally friendly technologies to market.

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Superviseur du corps professoral :

Costas Tzoganakis

Étudiant :

Partenaire :

Innovation Factory

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Waterloo

Programme :

Business Strategy Internship

LoRaSat: Implementation and Evaluation of a Satellite-based LoRaWAN Network

This project will study how well LoRaWAN can work for direct communication with Low Earth Orbit (LEO) satellites to connect IoT sensors in remote “no-coverage” areas. The intern will use a simulation tool to model thousands of devices sending data to a satellite during a single pass and will measure how often messages succeed or collide. The project will also test simple improvements to reduce collisions and increase reliability, such as using time slots (Slotted ALOHA) or listening before sending (CSMA). The expected benefits are clearer limits on how many devices a satellite LoRaWAN link can support, practical guidelines to improve reliability, and stronger solutions for low-cost connectivity in remote regions for applications like environmental monitoring, infrastructure sensing, and early warning systems.

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Superviseur du corps professoral :

Lokman Sboui;Kim Khoa Nguyen

Étudiant :

Partenaire :

École nationale d'ingénieurs de Sfax

Discipline :

Engineering

Secteur :

Information and Communications Technology (ICT); Aerospace

Université :

École de technologie supérieure

Programme :

Globalink Research Award

Deep Learning Approach to Automatic Detection of Tool Wear in Machining Using Coolants

The project advances the development of an AI-based system for automatic cutting tool condition characterization using machine learning and machine vision. It addresses the limitations of indirect tool wear monitoring by enabling direct, image-based wear analysis both on- and off-machine, with a focus on reducing downtime and improving tool utilization. A key challenge is adapting the system to industrial environments with cooling lubricants, which requires changing camera hardware and compensating for reduced image quality through advanced image enhancement techniques. Building on a previous model that for wear segmentation and classification under dry machining conditions, the project evaluates model weighting strategies and extends the approach to lubricated processes. The dataset and predictive maintenance models are expanded and re-evaluated to improve tool life prediction beyond simple linear regression. Overall, the project aims to deliver robust data pipelines, model architectures, and preprocessing methods that support accurate wear progression analysis and practical Industry 4.0 deployment

Voir la description complète du projet
Superviseur du corps professoral :

Dan Wu

Étudiant :

Partenaire :

Karlsruher Institut für Technologie

Discipline :

Computer science

Secteur :

Education

Université :

University of Windsor

Programme :

Globalink Research Award

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Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.