Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

31132 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9291
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

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.

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Faculty Supervisor:

Daryl Haggard

Student:

Partner:

Université Grenoble Alpes

Discipline:

Physics

Sector:

Education

University:

McGill University

Program:

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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Faculty Supervisor:

Jeremy Kerr

Student:

Partner:

Université de Picardie Jules Verne

Discipline:

Life Sciences

Sector:

Education

University:

University of Ottawa

Program:

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.

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Faculty Supervisor:

Maja Tarailo-Graovac

Student:

Partner:

Université de Strasbourg

Discipline:

Life Sciences

Sector:

Education

University:

University of Calgary

Program:

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

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Faculty Supervisor:

Alexandra Nemery

Student:

Partner:

Paralog

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

École de technologie supérieure

Program:

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.

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Faculty Supervisor:

Irene Cheng

Student:

Partner:

Innoflo

Discipline:

Computer science

Sector:

Mining

University:

University of Alberta

Program:

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.

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Faculty Supervisor:

Ebenezer Ekow Essel

Student:

Partner:

Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile

Discipline:

Engineering

Sector:

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

University:

Concordia University

Program:

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.

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Faculty Supervisor:

Gurjit Randhawa

Student:

Partner:

Vellore Institute of Technology

Discipline:

Computer science

Sector:

Quantum Science; Biotechnology; Artificial Intelligence

University:

University of Guelph

Program:

Globalink Research Award

Commercializing CO2-Derived Clay Polymer for Agricultural Applications

This project between McMaster University and Carbon Upcycling Technologies (CUT) aims to commercialize sustainable agricultural products made from captured CO2. The team will assess markets for sprayable clay–polymer coatings that protect crops from UV damage and clay-based soil conditioners that improve water and nutrient retention. The outcomes will guide pilot partnerships and help CUT scale climate-positive technologies that support both Canadian agriculture and environmental sustainability.

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Faculty Supervisor:

Todd Hoare

Student:

Partner:

Carbon Upcycling Technologies

Discipline:

Engineering

Sector:

Agriculture and Food; Biomanufacturing; Nanotechnology

University:

McMaster University

Program:

Business Strategy Internship

Reproducible Neuroimaging in Sport Science: Understanding Microstructural Brain Changes in Sprinters Undergoing Intense Athletic Training

Although it is known that exercise is beneficial to neurological health, there is a lack of research focusing on structural brain adaptations in athletes undergoing intense training. While neuroplasticity is often studied in clinical populations or general aging cohorts, this project aims to specifically investigate sprinters to identify exercise-related microstructural changes in the brain. As reproducibility in neuroimaging remains a major challenge, this project also seeks to deliver a reproducible framework through Neurolibre, an innovative platform developed by the project’s home supervisor to support open and ethical science.
The collaboration between Juntendo University and Polytechnique Montréal will create a unique bridge between Canadian methodological expertise, via Neurolibre, and Japanese experimental strengths, through access to multidisciplinary research environment and an athletic study population.

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Faculty Supervisor:

Nikola Stikov

Student:

Partner:

Juntendo University

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

Polytechnique Montréal

Program:

Globalink Research Award

Integrating Large-Scale Brain Simulations Executed on High-Performance Computing Systems into Reproducible Open Publishing Frameworks via NeuroLibre

This project will connect two areas of cutting-edge research : large-scale brain simulations performed on Japan’s supercomputers, and Canada’s NeuroLibre platform for open and reproducible publishing. The goal is to create a proof-of-concept workflow where complex brain simulations are run on high-performance computing systems, and their key results are transformed into interactive, user-friendly publications on NeuroLibre. This will allow researchers, students, and industry partners to explore advanced neuroscience results without needing direct access to supercomputers, making pioneering discoveries more accessible and reusable. The collaboration will benefit both institutions: Juntendo University will gain new ways of sharing results from its large-scale simulations, while Polytechnique Montreal and NeuroLibre will expand their leadership in open science by supporting global, data-intensive research.

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Faculty Supervisor:

Nikola Stikov

Student:

Partner:

Juntendo University

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

Polytechnique Montréal

Program:

Globalink Research Award

Data-Driven Disaggregation of HP Loads in Transmission Systems with Limited Data

This project develops AI- and data-driven methods to improve visibility of heat pump (HP) loads in power grids. By disaggregating HP consumption from aggregate smart meter and grid data, it will support accurate demand forecasting, reliable grid operation, and planning for heating electrification in cold climates, contributing to reduced greenhouse gas emissions.

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Faculty Supervisor:

Claudio Adrián Cañizares;Maurice Dusseault

Student:

Partner:

Karlsruher Institut für Technologie

Discipline:

Engineering

Sector:

Education

University:

University of Waterloo

Program:

Globalink Research Award

Real-time satellite telemetry anomaly detection for on-board autonomy

As the number of satellites in orbit grows rapidly, with constellations ranging from dozens to thousands of spacecraft, continuous human monitoring does not scale; ground contact is limited and quick autonomous responses are essential to protect mission assets and space safety. The project aims to create a fast, reliable method to automatically detect anomalies in small-satellite telemetry, enabling a spacecraft to monitor itself in real time. We will design simple yet informative indicators that condense streams from key sensors and subsystems (power, attitude control, thermal, communications) into compact features suitable for on-board processing. These features will feed a lightweight machine-learning model capable of flagging both known and previously unseen behaviors with minimal computing and power. Validation will use simulated and historical flight data across nominal, degraded and fault scenarios, verifying strict limits on latency, memory and energy. Evaluation will emphasize accuracy, low false-alarm rates and clear operator explanations. Expected benefits include safer, more reliable constellations, reduced ground workload, faster diagnosis and recovery, and reusable tools adaptable across platforms.

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Faculty Supervisor:

Jesus Gonzalez Llorente

Student:

Partner:

Universidad Mayor

Discipline:

Engineering

Sector:

Aerospace; Artificial Intelligence

University:

École de technologie supérieure

Program:

Globalink Research Award

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.