Projets novateurs réalisés

Bosonic Theory via Nonstandard Harmonic Analysis

The project is part of the PhD research conducted by Madeline Berezowski. It is also part of a multifaceted research program developed collaboratively by Dr. Sowa and Dr. Fransson. It revolves around the idea of developing custom mathematical methods in support of frontier quantum physics research. Our specific target is an analysis of systems of bosons and the anisotropic spin chains, and their quantum phase transitions. Our theoretical insights will support experimental inquiry into the fundamental properties of quantum matter. They will also furnish new paths toward the deployment of quantum-resource-enhanced technologies.

Integration of multi-omics data for predictive modelling of drug efficacy in cerebral organoids

To date, using only electrophysiological data, Epiloid has achieved a classification accuracy in the low-90% in classifying drugs on their predicted ability to modulate hyperexcitability (as seen in epilepsy). This project seeks to enhance this accuracy by incorporating multi-omic datasets into Epiloid’s machine learning pipeline, which stands to dramatically assist in collecting a holistic picture of how new therapeutics affect neural circuitry of brain organoids. This provides a useful translational tool towards personalized medicine.
Epiloid Biotechnology is pioneering the integration of human-derived 3D cerebral organoids with machine learning (ML) to enhance preclinical drug discovery for neurological diseases. This project aims to bridge the gap between complex transcriptomic readouts and ML-driven drug efficacy predictions, leveraging human-relevant in vitro models. In collaboration with the Stem Cell Network, this project will support the ongoing R&D efforts funded through Epiloid’s Ontario Genomics BioCreate grant, which focuses on
integrating multi-omics data into predictive models for neurological drug development. The project includes the development of human iPSC cultures, cerebral organoid generation, and optimization of of SOPs for transcriptomic data preprocessing for ML model integration, and validation of these models using publicly available datasets.
The project will deliver several critical outcomes, including optimized SOPs for transcriptomic data generation, high-quality organoid datasets, and enhanced ML models that integrate electrophysiological and transcriptomic data. These advancements will position Epiloid as a leader in human-relevant preclinical testing, directly supporting its long-term goal of becoming a key partner in precision drug discovery.Furthermore, this partnership aligns closely with SCN’s national mandate to accelerate the development of
regenerative medicine and cellular therapies through collaborative research.
Anticipated benefits include the improved prediction of drug efficacy and safety, reducing costly late-stage failures; accelerated drug discovery timelines through better preclinical models; and enhancing Epiloid’s competitive advantage through proprietary organoid and machine learning data pipelines.

AI model development for predicting powder coat thickness distributions

Surface coatings are an important part of many industries (e.g., aerospace, automotive) owing to their practical benefits. In particular, powder coating is now quite common given its ease of use, durability and eco-friendliness. Controlling the uniformity of the coating is highly desirable to improve the efficiency of the technique and to progress toward its automation. Powder coating presents a complex multiphysics problem. It is challenging to obtain a uniform coating thickness by optimizing the process parameters through simulation. This project aims to develop an AI-driven model to predict coating thickness distribution arising from arbitrary powder coating parameters.
NeurobotIA and Cadence want to use the AI model to develop and commercialize an automated powder coating technology as well as use the architecture to develop new applications (e.g., AI-assisted soldering). The vision of NeurobotIA is to continue to address labour shortages through technological solutions in collaboration with academic institutions, which will strengthen both industry and universities in Canada.

L’aide multicritère à la décision pour la priorisation des services au CIUSSS-CN

Ce projet vise à créer un outil d’aide à la décision pour aider le CIUSSS de la Capitale-Nationale à mieux prioriser ses services de santé. Il s’inscrit dans une démarche de recherche-action, en collaboration avec différents acteurs du milieu. L’outil reposera sur une méthode d’évaluation multicritère (AMCD) qui prend en compte des éléments à la fois quantitatifs (comme les ressources disponibles) et qualitatifs (comme les besoins des usagers). Ce type d’approche permet de concilier plusieurs points de vue et d’aboutir à un processus de décision plus juste, transparent et accepté socialement. L’objectif est de mieux adapter l’offre de services aux besoins réels de la population et aux capacités du CIUSSS, tout en respectant ses obligations. Le projet vise également à assurer une plus grande équité entre les usagers. En fin de compte, cela devrait améliorer l’efficacité du système et la qualité des soins pour tous les usagers.

Development of novel TiC based grain refiner for aluminum 319 alloy

The goal of this project is to develop a novel titanium carbide grain refiner for improving the strength of the lightweight aluminum 319 alloy. Enhanced use of lightweight alloys in automobiles, currently limited because of their strength, can enable auto industry to manufacture lighter and more fuel efficient vehicles. Titanium carbide, due to its ability to modify the structure of the casting during solidification, can significantly improve the strength of the 319 alloy. However the main challenge is to control the Ti:C ratio in the melt for effective modification. Thus, in this study, a pre alloyed ultrafine TiC powder will be added to the molten 319 alloy. Casting and mechanical testing will be carried out at USc. The stability of the TiC particles in the melt during casting solidification and their refinement mechanism will be studied by advanced characterization techniques using equipment available at laboratories at lISe

TRLUP–IPMNowApp

The proposed project will leverage AI technology and citizen science to improve the efficient and responsible use of pesticides in agriculture. Farmers and agronomists face significant challenges in identifying insect species in their crops. Many insects look similar, but while some are harmful pests causing severe crop losses, others are beneficial, acting as natural pest controllers or pollinators. Misidentification leads to unnecessary pesticide use, increased production costs, environmental harm, and reduced yields. There is a lack of accessible, real-time tools to support accurate insect identification and provide effective, sustainable pest management strategies.
We are developing a mobile application powered by artificial intelligence that enables users to identify insects instantly through a simple photo. The app, called “IPM Now”, distinguishes between pests, beneficial insects, and pollinators. It georeferenced each observation, contributing to a dynamic, crowd-sourced database of insect presence. Beyond identification, the app provides tailored integrated pest management recommendations based on the specific crop and location, promoting sustainable practices by combining cultural, biological, and chemical control options when necessary. Our solution empowers farmers, agronomists, and agricultural consultants with real-time, science-based decision-making tools. By reducing reliance on broad-spectrum pesticides and encouraging the conservation of beneficial insects, the app supports both economic and environmental sustainability. It has the potential to decrease crop losses, lower input costs, and enhance biodiversity in agroecosystems. Additionally, the growing database will offer valuable insights into pest and beneficial insect dynamics across regions, supporting research, policy-making, and precision agriculture initiatives.
Our business model is structured to scale over time, both in terms of features and geographic reach. Participating in the TRL program will provide us with the applied research environment and commercialization training needed to move IPM Now toward a higher Technology Readiness Level.

TRLUP preparing GradFinder for Launch: Enhancing AI, Security, and Usability for Graduate Recruitment

GradFinder is an AI-powered platform that connects students looking for thesis-based graduate positions with professors who are actively recruiting. Right now, students waste time searching dozens of university websites and sending cold emails, while professors are overwhelmed with unqualified inquiries. This project will help GradFinder move from a working beta version to a polished, scalable platform ready for launch across Canada. With support through this program, GradFinder will improve its AI matching system, strengthen data privacy and security, and build new tools to make the platform easier to use. These upgrades will help the company attract more users, build trust, and successfully bring its solution to market, saving time for both students and professors in the graduate recruitment process.

Évaluation des besoins des populations d’expression anglaise au Saguenay-Lac-Saint-Jean

Au cours du projet, le ou la stagiaire travaillera en collaboration avec l’organisme partenaire, ECO-02, pour construire des activités participatives inclusives permettant d’évaluer les besoins et les enjeux de cette population dans le but de proposer des solutions pour améliorer leur qualité de vie. La méthodologie comprend une période d’observation des activités du partenaire pour mieux adapter l’intervention, une revue de la littérature pour assurer l’inclusivité des activités, la conception et la réalisation de trois à quatre activités d’animation sur les enjeux et besoins de la communauté anglophone et une activité d’animation sur les pistes de solution. Les résultats obtenus serviront à l’élaboration de la prochaine planification stratégique d’ECO-02 et contribueront à assurer une meilleure qualité de vie pour la communauté anglophone du Saguenay-Lac-Saint-Jean.

TRLUP – Conceptual study of next-generation spacecraft components

The global space sector has seen massive growth in recent years, fueled by lower launch costs and increased commercial access. Yet, many spacecrafts still rely on decade-old hardware designs. Innovation in spacecraft components remains limited. The industry’s strong preference for “space heritage” makes it slow to adopt cutting-edge systems, even if they are flight-proven by major science missions like the James Webb Space Telescope.

In Canada, 93% of space companies are small to medium-sized enterprises (SMEs) and are disproportionately affected by this issue. While Canadian firms are highly active in R&D, many innovations become “technology orphans”. These are technically viable solutions that are commercially un-adopted due to limited flight heritage. Space agencies like NASA and the European Space Agency have launched technology demonstration programs to validate commercial off-the-shelf (COTS) or industrial components in space. This project explores how the outputs of that program can be repurposed to meet the needs of Canadian space SMEs and reduce the risk of “technology orphan” solutions in aerospace R&D.

The project will combine technology scouting, stakeholder interviews, and early-stage prototyping to identify and develop a minimum viable product (MVP) concept. The project will focus on optical components such as infrared cameras or multispectral imagers to reflect the intern’s academic background, though other concepts may be explored based on industry needs and opportunity. Through a structured 4-month program, the intern will deliver a business report, concept prototype, and pitch deck. These outputs will assess both the technical feasibility and market potential of selected concepts to build a practical bridge between under-utilized flight-ready technology and real business needs in Canada’s evolving space ecosystem.

Enhancing Vision Encoders for GUI Navigation with VLMs

(1) the main activities of the partner
ServiceNow develops a platform for client organizations to manage and automate large-scale processes across various industries. In 2020, ServiceNow acquired Element AI to strengthen its presence in the Artificial Intelligence (AI) research landscape and the Canadian AI ecosystem. This acquisition enabled the development of AI-driven products that improve the platform’s capabilities. ServiceNow Research has made significant contributions to the field of foundation models, notably in Natural Language Processing (NLP), and has a strong presence in developing generative models for different data domains.
(2) the challenges the partner aims to solve through this project
Navigating GUIs poses significant obstacles for AI agents, especially in ensuring generalization across diverse tasks and domains. Current vision-language models struggle with compositionality, grounding abstract plans within pixel-based interfaces, and recovering from errors. This project seeks to address these issues by developing modular architectures that enhance planning, grounding, and execution, advancing the robustness of GUI agents while integrating reinforcement learning to improve alignment between predicted actions and interface outcomes.
(3) the anticipated social or economic benefits of the project for the partner organization(s)
Developing effective web agents has the potential to enable various applications, such as automating administrative tasks, assisting new employees with tool usage during onboarding, and making web navigation more accessible for individuals with visual or other impairments. Progress in this area can lead to the creation of prototypes and proofs-of-concepts for ServiceNow, supporting the integration of web assistants into production and advancing the product roadmap.

Stage en stratégie marketing pour renforcer l’autorité de la marque Ludo et sa présence numérique

Le projet de recherche proposé vise à élaborer une stratégie marketing claire et efficace pour renforcer l’autorité de la marque Ludo et accroître sa visibilité auprès des entreprises. Le stagiaire travaillera à définir les bons messages, les bons canaux et les bons formats pour faire rayonner Ludo de manière cohérente et inspirante. Une attention particulière sera portée à la création d’une empreinte digitale durable et profitable, en développant du contenu engageant, en optimisant la présence en ligne (site web, infolettres, médias sociaux) et en analysant les retombées des actions mises en place. Ce projet permettra à Ludo de structurer ses efforts marketing, d’augmenter sa notoriété dans un marché concurrentiel et de mieux convertir les entreprises intéressées en clients fidèles.

Renforcer la compétitivité canadienne en renforçant les chaînes de valeur locales

This proposed study is an unprecedented attempt by a North American municipality to proactively identify and strengthen the components of its local value chain to inform a comprehensive strategy dedicated to growing opportunities in local advanced manufacturing, targeting increasingly interwoven value chains. Surrey has the space and existing capabilities to become an advanced manufacturing hub for Canada, growing supply chains and attracting participants, which in turn will drive on-shoring of high value manufacturing in the region. This study can also serve as a template for identifying and strengthening other supply chains across Canada. Utilizing an established methodology developed by the University of Cambridge in the United Kingdom (Kumar, Srai, Pattinson & Gregory, 2013), we hope to gain a comprehensive understanding of Surrey’s current value chain capabilities, particularly those associated with the Health Technology and Clean Technology sectors. Employing newly developed data visualization software, Firmogram by BluePrime Technologies, a Surrey-based advanced technology solutions company, this study will allow local firms to identify opportunities develop and extend their capacity and capabilities in line with local needs and allow the municipality to identify any gaps that need to be filled. It will also allow firms considering re-location to more easily identify opportunitiesand partnerships