Projets novateurs réalisés

Exploration of the Use of Artificial Intelligence in Small, Rural Northern Municipality

The proposed project aims to modernize the Township of Terrace Bay’s records and data management systems by integrating AI-powered tools that streamline operations and enhance decision-making. Currently reliant on manual processes and limited digital infrastructure, the municipality faces challenges in organizing, analyzing, and retrieving key service data. This initiative will introduce automated classification systems, predictive models for infrastructure planning, and a customer service chatbot to improve public engagement. By leveraging expertise in records management, data engineering, and AI applications, the project will reduce staff workload, improve service delivery, and provide Terrace Bay with scalable, cost-effective tools for long-term planning and operational efficiency

Development of Analog Integrate circuit design Course with Siemens EDA Tools

The project is to develop a course on analog integrated circuit design targeted on undergraduate student education and training.

Lifting Black Voices

In response to the City of Ottawa’s Anti-Racism Strategy by analyzing and highlighting the stories and lived experiences of Ottawa’s historical Black communities through newspaper articles. Two key sources, The Ottawa Journal and the Spectrum (a newspaper dedicated to Ottawa’s Black community) will be scanned by the intern to identify excerpts that reflect Black voices and experiences that have been left out of the mainstream media. With help from archivists, community advisors and an academic supervisor, the intern will generate indexes and reports to promote these stories to benefit future research. This project aids the City Archives in contributing to a more accurate and equitable account of history, while promoting an inclusive public understanding of historical Black voices in Ottawa.

Rational programming of antigen structure using DNA nanotechnology to enhance vaccine immunogenicity

This project seeks to investigate how the nanoscale structure and arrangement of antigens influences immune recognition and vaccine efficacy. Using DNA nanotechnology and precise structural engineering, we aim to establish a structure-function paradigm linking antigen conformation and valency to immunogenicity. Using cancer vaccines as a stringent testbed of tumor antigens, the design rules established under this project provide a promising foundation for potentially translatable optimization of nanovaccines for a variety of conditions. A deliberate emphasis upon nanoparticle modularity is a key component of this project and its aims to provide a streamlined approach to nanovaccine development. Enabling flexible integration of a diverse library of antigens allows for more rapid prototyping and high-throughput evaluation of immune interactions. This work aims to define a set of fundamental principles concerning antigen structure and arrangement that can guide the rational engineering and programming of future nanovaccines.

In-vitro digestibility, bioactive peptide generation and functional characterization of lupin protein isolates modified by high-voltage atmospheric cold plasma

This project explores how different processing methods affect the nutritional quality and health benefits of proteins from lupin, a sustainable legume crop. Lupin is rich in protein, environmentally resilient, and cost-effective to produce, making it an attractive alternative to meat and dairy proteins. However, the way proteins are extracted and processed can strongly influence how well they are digested and what health-promoting compounds they release.
The project will compare two extraction methods: the conventional alkaline method and an innovative green approach using deep eutectic solvents (DES). Some protein isolates will also be treated with high-voltage cold plasma (HVCP), a novel non-thermal technology that can enhance protein functionality. These proteins will then be tested in a dynamic in vitro digestion system that mimics human stomach and intestinal conditions. The study will identify and measure peptides released during digestion and evaluate their potential health benefits, including antioxidant and blood pressure-lowering activities.
By linking extraction and processing methods to digestibility and bioactivity, this research will support the development of high-value plant-based food ingredients. The collaboration between the University of Manitoba and the University of Hannover will also strengthen international partnerships, foster trainee exchanges, and create opportunities for future joint projects.

Deep Learning-Based Classification of Defects in Induction Brazed Copper Joints Using Infrared Thermal Imaging for Enhanced Joint Quality Monitoring

This project will develop a new system that uses infrared cameras and artificial intelligence to automatically detect defects in copper joints made by induction brazing, a process widely used in heat pump manufacturing. By capturing and analyzing the heat patterns that appear during brazing, the system can quickly identify problems such as cracks or poor filler flow without the need for expensive and time-consuming testing. The collaboration between the University of Manitoba and the University of Aveiro will combine expertise in smart manufacturing and brazing technologies to create a prototype real-time monitoring tool. This will help industry partners improve the quality and safety of heat pumps, reduce costly rework, and support the transition to more energy-efficient and sustainable heating and cooling systems.

Optimization and Scale-Up of a Biomanufacturing Process for Anellovirus-Based Vectors for Gene Therapy Applications

Gene therapy is a cutting-edge treatment that can fix or replace faulty genes to treat diseases, but producing the necessary gene delivery cargo, called vectors, is currently expensive and complex. Many of today’s vectors also trigger immune responses in patients, limiting their efficacy. This project explores a new type of vector made from a harmless human virus called anellovirus, which exhibit natural immune evasion properties, indicating a potential for repeat dosing and long-term therapeutic applications without causing immune responses. The goal is to produce these anellovirus-based vectors using bacteria instead of animal or human cells. The partner company, Theraphage Inc., holds the rights to a unique technology called the iPhAGE system, which engineers bacteria to produce anellovirus-based vectors. Partnering with Theraphage, the interns will help optimize and scale up the biomanufacturing process for producing these vectors. This will reduce manufacturing costs, support the development of safer gene therapies, and create new job opportunities in Canada’s growing biotechnology sector.

2026 FIFA World Cup Marketing Plan Evaluation for Ontario Soccer

This project will help Ontario Soccer make the most of the upcoming 2026 FIFA World Cup by evaluating how well their “GeneratiON26” marketing plan is working and suggesting ways to improve future efforts. With games being hosted in Toronto, the World Cup is a unique chance to grow interest in soccer across Ontario. A Mitacs Business Strategy intern will work with Ontario Soccer to assess the success of its FIFA-related marketing activities, and recommend new ideas to help more people get involved in the sport. The project will give Ontario Soccer useful tools and insights to guide future marketing and community outreach efforts beyond the World Cup.

Optimizing Steam Pyrolysis Parameters for Biochar Production from Invasive European Buckthorn

This MITACS research project tackles two critical environmental challenges: the costly removal of invasive plants and the improvement of degraded urban soils. Municipalities currently invest substantial resources in removing invasive European buckthorn, which is often burned or sent to landfills, releasing stored carbon back into the atmosphere. Additionally, many urban soils are compacted and lacking in organic carbon, which impairs both water infiltration and soil respiration.

The project will expand opportunities for Senti Solutions’ Steam Pyrolysis technology, using it to convert European buckthorn into biochar—a charcoal-like substance that improves soil health. Unlike traditional burning methods, steam pyrolysis offers precise control over temperature and processing time, allowing for the creation of biochar with tailored properties. This targeted biochar can be particularly beneficial for urban soil restoration, as it improves soil water retention and aeration, both of which are essential for the growth and maintenance of trees in cities.

The MITACS intern will spend four months at Senti’s Guelph facility testing various processing conditions, measuring energy efficiency, and analyzing the quality of the resulting biochar. This research will establish production protocols for biochar that meet international standards for soil amendments, while also rigorously documenting the commercial viability of this waste-to-value approach.

Towards Real-time Assessment of Drift Conditions during Potash Mining

The safety of underground mining operations depends heavily on monitoring and management of various hazards that can arise within mines and their supporting infrastructures, particularly the drifts (tunnels). Drifts are critical pathways within mines, and their structural integrity is paramount to ensure the safety of workers and equipment. Traditional methods of evaluating drift stability often involve manual inspection and/or manual collection of data from local instrumentation clusters. This can be time-consuming and limited in scope. The proposed research will explore the use of artificial intelligence (AI) technologies to assess the structural integrity of the drifts in the potash mines. It involves assessing the parameters and criteria required to identify the precursors to failure, as well as the means to collect those parameters in real-time. The findings from this research are expected to offer insights into the implementation of advanced AI solutions for hazard detection, maintenance scheduling and risk mitigation in mines.

Analysis of the electricity demand profile of a ski resort: characterization and identification of demand management opportunities

In recent years, Québec’s ski resorts have struggled with mounting electricity expenses. Warmer winters and growing stress on the provincial grid have driven up the energy needed for snow-making, particularly in the run-up to the holiday season. This significant cost is primarily driven by demand charges incurred from the intensive use of snow-making equipment prior to operating hours. As a result, ski resorts often spend tens of thousands of dollars on snow-making, which can account for 17% to 38% of their total electricity bill. This issue is expected to intensify in the near future, placing additional financial and operational strain on ski resorts in Québec and across Canada. While some ski station operators have begun exploring alternative control strategies and new technologies, there remains a pressing need for dedicated research into load management solutions for this sector. Previous studies have assessed the overall impact of snow-making at various resorts and documented mitigation strategies. The proposed project aims to: (a) conduct an on-site assessment of the electric load profiles of key circuits (snow-making, ski lifts, lighting) at a large ski resort, and (b) identify practical measures to reduce the resulting aggregated load.

Apprentissage machine quantique pour la prédiction de l’efficacité de nouveaux médicaments anti-inflammatoires : application pour la prédiction de l’affinité d’inhibiteurs de l’enzyme 5-lipoxygénase

Le développement d’une molécule de la conception jusqu’à l’obtention d’un médicament est un processus couteux. Dans ce processus, la première étape consiste à synthétiser une série de molécules qui inhibent une action biologique particulière, généralement médiée par un récepteur ou une enzyme. Les essais biologiques et cliniques peuvent être les étapes les plus couteuses de tout le processus de conception d’un médicament. Le recours à l’intelligence artificielle pour la modélisation moléculaire pourra réduire considérablement le temps et par conséquence les couts de cette importante étape de la découverte d’un médicament.
Parmi les cibles thérapeutiques d’intérêt, les lipoxygénases sont des enzymes cliniquement pertinentes qui sont responsables du métabolisme de l’acide arachidonique par l’insertion d’un atome d’oxygène. Parmi les pathologies liées aux métabolites générés par les lipoxygénases, on peut citer l’athérosclérose, l’asthme, la polyarthrite rhumatoïde et le cancer. En particulier, la 5-lipoxygénase (5-LO) semble être liée à ces maladies à composante inflammatoire, notamment les maladies des voies respiratoires telles que l’asthme et la rhinite.
Dans ce projet, nous proposons d’utiliser l’apprentissage automatique quantique pour simuler rapidement et avec précision l’affinité des inhibiteurs de la 5-LO. Cette approche représente une alternative prometteuse aux méthodes expérimentales traditionnelles, en accélérant la découverte de nouveaux traitements.