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Consider a question that a policymaker might have: which economic factors have causal effects on the median housing price in a region? Answering this question requires gathering historical observations of house prices and economic factors of interest and performing statistical analysis to asses causal effects. But what if the policymaker does not exactly know which economic factors are relevant? What if they cannot afford to measure some of them?

Intact Financial Corporation is Canada's largest provider of property and casualty insurance by annual premiums. Intact aims to offer expedited claims service to its customers. When opening a claim, Intact already asks its customers to provide images of the vehicle that allow prior identification of the general condition of the vehicle. The intern will have to extract the information automatically from the images that will be attached to the file. Intact then wants to explore different approach through supervised machine learning to shorten the steps deemed critical in the claims process.

Engineering organisations like Thales rely on large quantities of technical knowledge. When resolving a technical
problem, for example, users have to follow a multi-step procedure in which the steps are described with various
levels of detail, may not be up to date, or may not target the exact problem they are facing. Recent progress in
Large Language Models (LLM) showed capabilities for these models to reason over procedural knowledge but it
is still very difficult to evaluate if these models will be able to support users in executing complex, procedural tasks
in various scenarios.

On cherche a prevoir via l'intelligence artificielle (IA) comment un groupe d'equipements de moyenne tension appele «
paste satellite » va alimenter en electricite son secteur de distribution pour les prochaines heures et jours.

Autonomous vehicles operating at Level 4 autonomy require a comprehensive understanding of their current driving situation and context. While current systems rely mainly on perceptual information such as video, lidar, and radar, they often lack the necessary understanding of the vehicle's concrete situation. To address this gap, this research project aims to develop a foundational model for scene understanding in the context of autonomous driving.

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Dreeven platform serves a diverse range of construction companies, providing training and support during their onboarding process. Client adoption varies based on their needs and interests, with some fully engaging while others become disengaged and cancel their subscription. To address this, machine learning will be used to predict key factors influencing user adoption at different stages of interaction. Analyzing user log data will identify adoption patterns, allowing for user segmentation and tailored marketing.

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En partenariat avec le laboratoire en aquaponie de la compagnie ÉAU (Écosystèmes Alimentaires Urbains), le projet vise à développer des outils d’optimisation mathématique permettant de minimiser les coûts financiers et environnementaux de fonctionnement tout en assurant la résilience du système en prenant en compte les diverses contraintes présentes. Le stage considère la modélisation mathématique des processus en oeuvre afin de permettre leur intégration dans des modèles de simulation et des programmes mathématiques d’optimisation.