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

Porous Structure Modeling

This project focuses on developing better computer models to predict how recycled concrete can store and transfer heat for use in thermal energy storage (TES) systems. Instead of relying only on expensive and time-consuming experiments, the project will create more accurate and efficient simulation methods that can speed up the design of sustainable energy storage devices. For the participating institutions, the Czech Technical University in Prague will gain advanced modeling expertise from the University of Waterloo, improving the quality and range of its experimental research. Meanwhile, Waterloo will apply its modeling tools and methods to a new material class, expanding its research impact and opening opportunities for collaboration in sustainable construction and renewable energy storage. Together, both institutions will strengthen their research capabilities, foster international collaboration, and contribute to solutions that support clean energy and circular economy goals.

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Superviseur du corps professoral :

Jeff Gostick

Étudiant :

Partenaire :

Czech Technical University in Prague

Discipline :

Engineering

Secteur :

Sustainability and the Environment; Manufacturing and Construction; Clean Technology

Université :

University of Waterloo

Programme :

Globalink Research Award

Neural Importance Sampling for Layered BSDFs

This project aims to improve the simulation of complex materials such as layered paint, fabrics and coated metals in computer graphics applications. We will develop a neural network-based sampling model that allows accurate and efficient light transport within these materials. In addition, we will integrate this model with general path-guiding techniques to minimize noise on both microscopic and macroscopic levels of light interaction. This collaboration strengthens ongoing research between ÉTS (Canada) and XLIM (France), and provides advanced training for students in modern rendering methods that are directly compatible with digital content creation pipelines in animation, visual effects and video games.

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Superviseur du corps professoral :

Adrien Gruson

Étudiant :

Partenaire :

Université de Poitiers

Discipline :

Computer science

Secteur :

Education

Université :

École de technologie supérieure

Programme :

Globalink Research Award

Plateforme de surveillance des comptes en fidéicommis

Les comptes en fidéicommis jouent un rôle essentiel dans la pratique notariale en garantissant la sécurité des fonds confiés par les clients jusqu’à leur affectation finale. Ils constituent un mécanisme de protection du public et de préservation de la confiance envers les notaires. Cependant, leur gestion soulève des enjeux complexes de traçabilité, de conformité réglementaire et de prévention des abus financiers. Bien que des procédures de vérification et des mécanismes d’audit existent déjà, ceux-ci reposent largement sur des contrôles a posteriori, ce qui limite la capacité de détecter en temps réel des anomalies ou des utilisations inappropriées des fonds.
C’est dans ce contexte que la Chambre des notaires du Québec – un ordre professionnel dont la mission est la protection du public – s’intéresse aux recherches sur l’usage de la chaîne de blocs comme solution technologique pour la traçabilité et la sécurisation des transactions. Le mandat confié consiste à concevoir, développer et mettre en œuvre une solution technologique innovante pour la surveillance des transactions financières effectuées sur les comptes en fidéicommis des notaires.

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Superviseur du corps professoral :

Charlaine Bouchard;Kaiwen Zhang

Étudiant :

Partenaire :

Chambre des notaires du Québec

Discipline :

Sociology

Secteur :

Other services (except public administration); Professional, scientific and technical services

Université :

Université Laval

Programme :

Accelerate

Design and Development of Quantum Entanglement-Enabled Multiparty Key Distribution Protocol for Secure Communication

Quantum communication stands at the frontier of next-generation secure communication systems, leveraging the principles of quantum mechanics to address the limitations of classical cryptography. Among quantum-based techniques, Quantum Key Distribution (QKD) has emerged as a revolutionary solution enabling provably secure key exchange. While bipartite QKD protocols (such as BB84) are well-studied, there remains a significant gap in realizing multiparty quantum key distribution (MQKD) protocols essential for secure communication across complex, distributed, and scalable network infrastructures.
The proposed project, titled “Quantum Entanglement-Enabled Multiparty Key Distribution protocol for secure communication”, aims to design and develop a novel entanglement-based MQKD protocol that will address current scalability and security challenges in quantum networks. This protocol will exploit multipartite entanglement resources to establish secure keys across multiple parties simultaneously, significantly enhancing communication security in applications such as IoT, autonomous systems, smart grids, and critical infrastructure protection.
The novelty of this work lies in architecting a distributed, scalable MQKD protocol leveraging high-dimensional entanglement states, dynamic network topologies, and quantum repeaters to ensure long-distance, loss-tolerant, and robust secure communication channels. The anticipated outcome will pioneer new frontiers in quantum-secured multiparty communications, addressing both academic and industry demands and global quantum technology development.

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Superviseur du corps professoral :

Georgiy Krylov

Étudiant :

Partenaire :

Universiti Malaysia Perlis

Discipline :

Computer science

Secteur :

Quantum Science; Information and Communications Technology (ICT); Cyber Security

Université :

University of New Brunswick

Programme :

Globalink Research Award

AI-Driven Compliance Automation Platform for Cybersecurity and Privacy Standards

Organizations today face increasing difficulty keeping up with cybersecurity and privacy rules such as ISO 27001, SOC 2, GDPR, and Canada’s PIPEDA. Meeting these requirements is often slow, expensive, and prone to human error because compliance checks are still done mostly by hand. To address this, Carleton University and Sabytel Technologies Inc. are working together to build an advanced Artificial Intelligence (AI)–powered compliance platform called GRCCloud9. The project will use AI tools such as natural language processing, computer vision, and probabilistic reasoning to automatically collect, verify, and assess compliance evidence. This will help companies prepare for audits faster, more accurately, and at lower cost. For Sabytel, the result will be a smarter, scalable, and market-ready compliance solution. For Canada, it will build AI expertise, strengthen cybersecurity practices, and support organizations in staying compliant with global standards more efficiently and securely.

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Superviseur du corps professoral :

Masoud Barati

Étudiant :

Partenaire :

Sabytel Technologies Inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Carleton University

Programme :

Accelerate

Searching for thermal electrons in the relativistic tidal disruption event Swift J1644+57

Tidal Disruption Events (TDE) occur when a star travels too close to a supermassive black hole. During a TDE, the star is ripped apart due to the gravitational pull of the black hole and produces a bright multi-wavelength radiation that evolves over a timescale of years. The first observation of a TDE was made in the 1990s, and since the substantial strides have been made in our understanding. Contrary to popular believe, if is very hard to funnel material into a black hole and so when a star is disrupted the black hole ejects a lot of the stellar material at velocities close to the speed of light. As the ejected material travels away from the supermassive black holes it produces radio light. In the proposed project, the student will use observations of a TDE discovered in made at radio frequencies studying the content of the ejected material and to understand the impact that a TDE can have on the environment surrounding the supermassive black hole.

Voir la description complète du projet
Superviseur du corps professoral :

Daryl Haggard

Étudiant :

Partenaire :

Université Grenoble Alpes

Discipline :

Physics

Secteur :

Education

Université :

McGill University

Programme :

Globalink Research Award

Comparing the performance of UAV-derived statistical models with established mechanistic models to measure microclimatic conditions in open and closed canopy environments

Climate change is reshaping ecosystems, yet regional climate data often overlook the fine-scale temperature patterns that species actually experience. These local variations, or microclimates, are influenced by vegetation, topography, and land cover, and can shield organisms from extreme conditions. This project aims to improve how we model and map microclimates using high-resolution drone imagery. We will develop statistical models that transform thermal infrared (TIR) and landscape data such as canopy cover, land cover, and landscape heterogeneity into detailed maps of near-surface air temperature. Using hourly drone imagery and ground air temperature records, we will compare these statistical models to established mechanistic models that simulate temperature from physical processes. By testing both open and forested habitats, we will identify how vegetation affects model accuracy and determine the best approach for capturing real microclimate variation. This collaboration combines the intern’s unique expertise in drone-based thermal mapping with Dr. Lenoir’s experience in forest microclimate modelling, strengthening research capacity at both institutions. The project will advance the use of remote sensing in ecological monitoring, contribute new methods for predicting how species respond to climate change locally, and create valuable international partnerships that help develop microclimate science in Canada and introduce new methods abroad.

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Superviseur du corps professoral :

Jeremy Kerr

Étudiant :

Partenaire :

Université de Picardie Jules Verne

Discipline :

Life Sciences

Secteur :

Education

Université :

University of Ottawa

Programme :

Globalink Research Award

Genetic modifiers in neurodevelopmental diseases: a C. elegans high-throughput forward genetic screen to identify suppressors of alg-1/AGO1

Rare diseases, despite being individually rare, cumulatively affect millions of people around the world. Over the last decades, high throughput sequencing elevated diagnostic success in rare diseases from ~10% to ~45% but have stalled at ~45% since. One reason for that may be that clinical presentation of the disease-causing alteration(s) could be highly variable. Some individuals may be severely affected while others may not be affected at all. Variants in disease-associated genes rarely act alone and play in concert with other variants in the genome, some of which enhance while some suppress disease presentations. These other variants that modulate the disease outcomes are called genetic modifiers and despite technological advances are extremely difficult to study using human genomes alone. One of the diseases where genetic modifiers are expected to play a role in documented phenotypic variability is Argonaute syndrome that is due to deficiency in the AGO1. To facilitate identification of genetic modifiers in patients with the Argonaute syndrome, we will utilize high throughput genetic modifier screens in a multicellular model organism Caenorhabditis elegans at the University of Calgary which could help identify targets and narrow the search space for modifier detection in patients with variable phenotypes at Strasbourg University.

Voir la description complète du projet
Superviseur du corps professoral :

Maja Tarailo-Graovac

Étudiant :

Partenaire :

Université de Strasbourg

Discipline :

Life Sciences

Secteur :

Education

Université :

University of Calgary

Programme :

Globalink Research Award

Ergonomie prospective : concevoir l’outil du futur pour l’IA comportementale des PNJ

Ce projet vise à révolutionner la conception des comportements des personnages non-joueurs (PNJ) dans le jeu vidéo. Actuellement, les outils d’annotation, utilisés pour définir les déplacements et les réactions, sont souvent trop techniques et basés sur du code. Cette complexité génère une charge cognitive importante et entrave la créativité des designers.
Inspiré par les outils d’analyse et de stratégie sportive, comme les tableaux tactiques, ce stage de recherche a pour objectif de définir la future génération d’outils. L’ambition est d’explorer de nouveaux paradigmes d’interaction, plus intuitifs et puissants, en alliant ergonomie cognitive et innovation.
La mission principale du stagiaire sera de mener un projet complet de design UX, de la recherche à la validation utilisateur. Le plan de mission, d’une durée de six mois, est structuré en trois phases. Les deux premiers mois seront dédiés à la recherche exploratoire : une analyse comparative d’outils sportifs et de développement de jeux, suivie d’entretiens avec des designers pour identifier leurs difficultés. La synthèse produira un rapport détaillé avec des personas et des axes d’innovation. Les mois trois et quatre se concentreront sur la conception. Le stagiaire organisera des ateliers de créativité pour générer des concepts innovants, puis concevra des scénarios

Voir la description complète du projet
Superviseur du corps professoral :

Alexandra Nemery

Étudiant :

Partenaire :

Paralog

Discipline :

Sociology

Secteur :

Professional, scientific and technical services

Université :

École de technologie supérieure

Programme :

Accelerate

Mining Smarter: Hydrocarbon Usage Optimization

At present, the data collected from the Fuel Management System (FMS) is only being used to a fraction of its potential. Our proposed solution is to delve into FMS data along with maintenance and operations information on critical mine site equipment such as heavy haul trucks, excavation equipment, and their light vehicle fleet to optimize the use of fuels and lubricants. This information coupled with GPS data from fleet vehicles would allow us to ensure every drop of these valuable and environmentally damaging resources are used only when needed and to their full potential. Additionally, this data will allow for extremely accurate calculation of GHG emissions from vehicle fleets and the impacts of fuel quality, driver habits, terrain, and preventative maintenance cycles on overall hydrocarbon emissions.
We will use machine learning and artificial intelligence to analyze FMS data and integrate it with other data sources on mine sites. A software solution will be offered to minesites which prioritizes opportunities to be actioned with key performance indicators monitored to ensure a reduction in consumption of hydrocarbons.

Voir la description complète du projet
Superviseur du corps professoral :

Irene Cheng

Étudiant :

Partenaire :

Innoflo

Discipline :

Computer science

Secteur :

Mining

Université :

University of Alberta

Programme :

Accelerate

Numerical Investigation of Particle Dispersion Behind Road Vehicles

Air pollution from road transportation is a serious environmental and public health concern, with significant economic impacts related to healthcare costs and air quality management. Ultrafine particles (UFPs), emitted by both exhaust and non-exhaust vehicle sources, pose severe health risks because their tiny size allows them to penetrate deep into the lungs. Their dispersion is strongly influenced by the shape and design of vehicles; however, current understanding of UFP behavior behind passenger cars and heavy-duty trucks is still limited. This project will combine computational fluid dynamics (CFD) simulations with wind tunnel experiments to investigate vehicle geometry effects on UFP dispersion. The findings will support the development of innovative strategies to reduce pollutant exposure, improve air quality, and guide cleaner vehicle design. This project will enhance Canada’s innovation capacity in sustainable mobility and contribute to global goals for better health, cleaner cities, and climate action.

Voir la description complète du projet
Superviseur du corps professoral :

Ebenezer Ekow Essel

Étudiant :

Partenaire :

Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile

Discipline :

Engineering

Secteur :

Transportation (excluding aerospace); Health and Related Sciences and Technology; Automotive

Université :

Concordia University

Programme :

Globalink Research Award

Quantum Machine Learning for Computational Drug Discovery

Drug discovery is traditionally slow, costly, and high-risk, with timelines exceeding a decade and expenses reaching billions. This lag is particularly problematic for neglected diseases and cardiovascular conditions, which carry significant global burdens but attract limited research investment. To address this, the project proposes leveraging quantum computing and hybrid quantum-classical algorithms to accelerate drug discovery. By integrating large-scale biomedical, chemical, and epidemiological data with Quantum Enhanced Machine Learning, the initiative aims to establish a framework for data-driven therapeutic development at the host campus.
Central to the project is rigorous data acquisition and curation. Datasets will be drawn from international repositories and Canadian healthcare sources, with strong pipelines for cleaning, annotating, and ensuring ethical handling. The project will focus on exploring emerging and neglected diseases with high potential for novel drug discovery, including alveolar echinococcosis, Oropouche virus, Lyme disease, West Nile virus, as well as critical areas in cardiovascular disorders. Quantum- enhanced models trained on these curated datasets could identify subtle patterns, forecast therapeutic outcomes, and uncover opportunities for drug repurposing.

Voir la description complète du projet
Superviseur du corps professoral :

Gurjit Randhawa

Étudiant :

Partenaire :

Vellore Institute of Technology

Discipline :

Computer science

Secteur :

Quantum Science; Biotechnology; Artificial Intelligence

Université :

University of Guelph

Programme :

Globalink Research Award

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