This project aims to develop new machine learning models that can help wind energy companies design wind farms more efficiently and at lower cost. Today, planning a wind farm requires running large, high-resolution computer simulations to understand how wind flows around turbines and how much power a proposed layout can produce. These simulations are accurate […]

This project develops mathematical models to investigate how tumour heterogeneity and oxygen availability influence the efficacy of oncolytic virotherapy (OVT). OVT employs genetically engineered viruses to selectively infect and destroy cancer cells, yet its therapeutic success is often hindered in hypoxic tumour regions, where low oxygen levels promote cellular adaptations that reduce viral replication and […]

This project explores how to better model gas flows in situations where traditional fluid equations break down, such as in very small systems or low-pressure environments. Instead of relying on costly molecular simulations, the research develops advanced extensions of classical fluid models that remain mathematically stable and thermodynamically consistent, while being flexible enough to describe […]

While telematics data with measurements of specific driving behaviors such as speeding, breaking, and turning, have shown significant predictive power in modeling accident risk, its usability has been challenged due to the low proportion of auto insurance policyholders that agree to provide their telematics data to the insurance companies. In this regard, we propose a […]

Les sécheresses extrêmes constituent un enjeu majeur dans un contexte de réchauffement climatique, affectant l’agriculture, les ressources en eau, la production énergétique et les écosystèmes. Leur caractère multi-échelle, dépendant des interactions entre précipitations, évapotranspiration, humidité du sol et stockage souterrain, rend leur détection et leur prévision complexes. Les indices traditionnels (SPI, SPEI, SSI) apportent une […]

Nowadays, many young Canadians [2] and newcomers [1] begin their financial journey with limited access to guidance on saving and investing, which leaves them at risk of debt and missed opportunities to build wealth. Existing tools are often complex and designed for experienced investors, not beginners. This project aims to develop and validate GradientX, an […]

The project focuses on Variational Mean-Field Games: it will provide theoretical guarantees for the convergence of numerical algorithms currently used and possibly develop also novel machine learning methods for it.

Beeye développe des algorithmes avancés d’optimisation automatisée des tâches, spécialement conçus pour résoudre des problématiques complexes liées à la gestion des ressources. Ses activités principales comprennent la création d’un système de planification automatisé qui s’attaque au problème de la planification multi-objectifs sous contraintes (MORCPSP). Ce système permet de gérer efficacement l’allocation des ressources, tout en […]

This project address the so-called Quantum Marginal Problem from a mathematical, physics and computational perspective. It develops the mathematical theory of optimal transport adapt to quantum states in order to make fundamental advances in physical aspects and computational algorithms solving the quantum marginal problem.

This project aims to develop new driving protocols and investigate the exotic physical properties of hyperbolic quantum materials using time-periodically modulated (“Floquet”) electrical circuits under magnetic driving. By leveraging these programmable circuits, we will explore how curvature and periodic modulation influence energy flow, spectral features, and effective magnetic behavior in synthetic hyperbolic lattices. The collaboration […]

Picton Mahoney Asset Management (“PICTON Investments”) was founded in 2004 to provide unique investment solutions to institutional, retail, and high-net-worth investors in Canada and globally. The Quantitative Research and Risk team at PICTON Investments is dedicated to developing and maintaining models that support investment decisions and risk assessments. With the increasing complexity of financial markets […]

Cortland Credit is a rapidly growing alternative investment manager specializing in private debt and public bond portfolios. Five years ago, the firm operated with fewer than 15 employees; today, the team has expanded to over 30 professionals. With this growth, the firm’s existing data management system – based primarily on Excel and CSV files stored […]