Projets novateurs réalisés

Interactions entre l’utilisation d’ultrasons et la rugosité des électrodes pour optimiser la production d’hydrogène en milieu alcalin

L’hydrogène est indispensable à de nombreux secteurs industriels et sa demande augmente, notamment pour des usages énergétiques. Pourtant, moins de 1 % de la production mondiale est issue de méthodes à faibles émissions, ce qui engendre d’importantes émissions de CO2. Parmi les technologies propres, l’électrolyse alcaline se distingue par son potentiel industriel et son recours à des matériaux peu coûteux comme le nickel.

Cependant, cette technologie souffre d’une perte de performance liée à l’accumulation de bulles de gaz sur les électrodes. Pour y remédier, deux approches existent : les stratégies passives, qui optimisent la structure de l’électrocatalyseur, et les stratégies actives, basées sur l’application de forces externes telles que les ultrasons. La combinaison de ces deux approches peut offrir des effets synergiques, comme l’ont déjà montré des travaux antérieurs.

Le projet vise donc à améliorer les méthodes de caractérisation des électrodes afin d’optimiser l’utilisation conjointe de surfaces rugueuses et d’ultrasons pour améliorer les performances de l’électrolyse alcaline.

Towards Personalized Screening: An AI Framework for Individual Breast Cancer Risk Assessment from MRI

The project aims to develop a deep learning framework for individualized breast cancer risk prediction from MRI, addressing the limitations of current “one-size-fits-all” screening procedures. The project is structured into three objectives:

1. The first objective involves implementing a deep learning framework to train a model on a large, unlabeled dataset of breast MRI volumes from Sunnybrook. This process will focus on making sure that the model is able to learn robust features directly from the data.

2. The utility of this model will be quantitatively assessed by fine-tuning it for specific downstream tasks, namely breast lesion and breast tissue segmentation. The performance will be benchmarked against a model trained from scratch, with our metrics being segmentation accuracy, which will validate the quality of our pre-trained model.

3. The final and primary objective is to leverage the pre-trained model for risk prediction. It will be used to train a predictive model that predicts the probability of breast cancer being developed. The core hypothesis, based on prior work in mammography, is that a high prediction score in a scan from a woman, without a current cancer diagnosis, correlates with a higher future risk.

Strategies for Protecting and Sharing Indigenous Sky Knowledges Gathered as part of a Collaborative Indigenous Fulldome Film Project

This internship proposal aims to support Nikan Heritage in its efforts to protect and pass on Indigenous astronomical knowledges retrieved through the research, development, and production phases of an immersive FullDome (360-degree) animated film. This film project focuses on the astronomical and Sky knowledge of First Peoples from northeast of Turtle Island (North America). The internships focus on four innovation and operational challenges: (1) inventorying and sharing back research material and footage used to produce the film; (2) developing educational material as complement to the film experience; (3) supporting cultural mediators who will discuss the film and its connections to Indigenous astronomical heritage at the Montreal Planetarium and, eventually, as part of a film tour planned for summer 2027 in Indigenous communities; and (4) evaluating these strategies for sharing and passing on Indigenous sky and star knowledge. These processes will support, in collaboration with members of the project’s advisory circles, the creation of video clips and a digital multimedia space complementary to the film, highlighting the people and communities who have contributed to the film with their insights, talents and stories. Interns will support the development of training sessions with the film’s cultural and scientific mediators. The internships will provide an opportunity to evaluate the benefits and impacts of these strategies.

Intégration de modules électromécanique et logicielles appliqué au développement d’un prototype de cytométre d’imagerie déclenché par mesure d’impédance.

Malgré un important développement d’instruments de mesures de microparticules dans le milieu de la recherche en océanographie et en écologie marine, aucun instrument n’a encore été développé pour collecter des données permettant la caractérisation de microorganismes individuelles de manière automatisée, directement dans les étendus d’eau, et à bas-prix. Un tel outil pourrait considérablement renforcer la capacité et la scalabilité des techniques de surveillance environnementale liées à la qualité de l’eau.

Ce type d’instrument serait utile de plusieurs manières pour préserver les écosystèmes marins au Canada, notamment afin d’étudier les types et les concentrations de divers types de microorganismes significatifs pour les milieux marins, d’être avertis de la prolifération d’espèces nocives comme des cyanobactéries ou algues toxiques, ou pour monitorer l’augmentation des concentrations de microplastiques dans les cours d’eau sur plusieurs mois.

L’objectif principal de ce projet de recherche consiste donc à créer le premier instrument de mesure permettant la caractérisation de microparticules aquatiques respectant toutes les contraintes suivantes: (1) excellente portabilité, (2) non-invasif pour les microparticules traitées et l’environnement, (3) disponible à bas prix, (4) autonome et automatisé, ne nécessitant aucune intervention humaine pendant l’utilisation, et (5) utilisable directement dans les étendus d’eau.

Integrated Mental Health Care for Refugees in a Primary Health Care Setting: What works, for whom and why

Mental ill health is a leading cause of disability worldwide and is significantly higher among refugees and asylum seekers due to trauma, displacement, and resettlement stress. Primary healthcare clinics are often the first point of contact for newcomers including refugees, however many refugees experience barriers in accessing the mental health resources and supports they need. In addition, due to the complexity of mental health needs, many providers find it difficulty to provide integrated mental health services.

This project explores how, why, for whom, and in what contexts integrated mental health care works for refugees within and across primary health care settings. Building on a five-year realist evaluation funded by the Michael Smith Health Research BC (MSHRBC) Scholar Award, the study partners with primary healthcare centers in BC to test and refine a practical model of integrated care. Through interviews, fieldwork, and collaboration with healthcare providers and service users, i.e., refugees, the project aims to identify what makes integration successful in real-world settings.

Findings will guide the development of an evidence-based framework to support more equitable, person-centered, and coordinated mental health services for refugees, strengthening both policy and practice across BC and Canada.

Fenix Alma Solutions Inc. Marketing Internship

This project will help Fenix Alma Solutions Inc. strengthen its marketing and outreach efforts for VidaNovaVLE®, a Canadian-built platform that supports medical and health-sciences education. Working with our leadership team, the intern will help create clearer messaging, improved resources for prospective partners, and better systems for tracking and engaging new leads. The project will also develop tools that make it easier for schools to understand our value, improving how we communicate our features, benefits, and real-world impact. By building these processes and materials, the project will support Fenix Alma’s growth, enhance our ability to reach new institutions, and contribute to the continued development of innovative education technology in Canada.

Operational Fleet Optimization for Offshore Logistics

This project aims to develop a smart and efficient method for determining how many offshore support vessels (OSVs) are truly needed to safely and reliably service offshore facilities such as FPSOs and drilling rigs. By analyzing factors like travel distance, weather, vessel capacity, and offloading schedules, the project will create a practical tool that helps the partner organization reduce unnecessary vessel use while maintaining safe operations. The expected benefit is a significant reduction in operating costs, better planning of marine logistics, and improved overall efficiency in offshore operations.

Stories in the Soil: sedaDNA analysis of cultural deposits at a ~10,000 year old archaeological site in the Lower Fraser River Basin

This project uses sedimentary ancient DNA (sedaDNA), a new method that recovers tiny traces of ancient DNA from soil, to learn more about past cultures and environments. Although sedaDNA is becoming common in academic research, it is rarely used in commercial archaeology. This project partners intern Angela Buttress with Inlailawatash LP, an archaeological consulting company owned by the Tsleil-Waututh Nation, to apply this technique at DhRr-928, an archaeological site in Metro Vancouver that is nearly 10,000 years old. By extracting and sequencing ancient DNA preserved in soil, the study will identify a wider range of plants and animals than traditional methods alone. This internship will benefit Inlailawatash by providing new, high-resolution environmental information that can strengthen cultural interpretations, support community-led decision making, and demonstrate the value of sedaDNA for future commercial projects. The project advances understanding of ancient ecosystems while directly benefiting the Nation’s archaeological and stewardship work.

Advancing the Hidden Subgroup Problem and its Variants

The Hidden Subgroup Problem (HSP) is a foundational open question in quantum computing with far reaching applications. Special cases of the HSP play a pivotal rule in groundbreaking quantum algorithms such as the celebrated Shor’s integer factorization problem (which is based on the abelian group case of the HSP) and the Graph Isomorphism Problem (which follows from the permutation group case of the HSP). Variants of the HSP have a variety of applications in quantum algorithms. For in-stance, the Hidden Vector Problem has been used in Learning with Errors. This project will investigate how tools from the representation theory of finite groups can be applied to the HSP and its variants. Representation-theoretic methods—such as Fourier analysis on groups, decomposition of representations, and character-based measurement strategies—play a major role in the most successful approaches to HSP. One of our goals is to explore if more advanced algebraic tools (such as the construction of characters using coadjoint orbits) can be utilized to yield stronger results for the HSP or its variants.

Automation versus Human Labour: A Cost-Modelling and Skill-Gap Analysis for Smart Supply Chain Transformation via the HI-TOP Lens

Automation and digital technologies are rapidly transforming supply chains, but organizations often struggle to determine when automation is cost-effective and when human labour remains the preferable option. Poorly aligned automation investments can increase costs, create skill shortages, and disrupt workers rather than improve performance. This project addresses that challenge by developing practical tools to help organizations make evidence-based, human-centred automation decisions. The project examines the trade-offs between automation, human-intensive, and hybrid supply-chain operations using the HI-TOP (Harmonized Information-Technology and Organizational Performance) framework. HI-TOP emphasizes that successful transformation depends on alignment between workforce skills, information systems, and organizational processes. Using surveys, interviews, and analyses of job postings from Canada and India, the project will examine how automation affects costs, skill requirements, and organizational readiness across different supply-chain contexts.

The main outcome will be a techno-economic and human-capital decision framework that helps organizations evaluate the true costs and benefits of automation, including training, process redesign, and change-management costs. The project will also identify priority skill gaps and upskilling pathways, supporting smoother workforce transitions and more inclusive digital transformation.

Acide lévulinique de biomasse forestière : une voie vers la production durable de Me-THF

La valorisation de la biomasse lignocellulosique en produits chimiques biosourcés nécessite des procédés efficaces de conversion en molécules plateforme. Le 2-méthyltétrahydrofurane (2Me-THF) est un solvant vert et un carburant avancé à fort potentiel industriel, mais sa production directe à partir des sucres C5 et C6 demeure limitée par l’absence de catalyseurs sélectifs à base de métaux non nobles. Une collaboration entre CRB Innovations et le CEPROCQ a permis d’obtenir des résultats préliminaires prometteurs, notamment l’isolement du furfural et sa conversion en 2Me-THF. Toutefois, la faible teneur en sucres C5 limite le rendement global rapporté à la biomasse initiale (˜2%). L’optimisation du procédé FIRSST permet désormais de valoriser également les sucres C6 via l’acide lévulinique, ouvrant la voie à un procédé intégré de production de 2Me-THF biosourcé.

Ontology-based Host Similarity for Predicting Virus Host Range

This project explores how the biological relatedness of species—measured using the official “tree of life” taxonomy—can help predict which animals or plants a virus might infect. The student will build a similarity map between organisms using public databases, then test whether viruses that infect one species are likely to infect its close relatives. This research could help scientists better understand how viruses spread across species and design better early-warning systems for emerging diseases. The project combines computer science and biology, giving the student valuable interdisciplinary training, and strengthens collaboration between the participating universities in AI-driven health research.