This project aims to extract musculoskeletal health information from medical MRI scans that are already routinely collected in spine cancer patients at Sunnybrook Health Sciences Centre. Patients treated for spinal tumors typically undergo both CT and MRI scans as part of standard care. In this project, the intern will develop and validate an automated image-analysis […]

The management of the lifecycle of artificial intelligence (AI) models—commonly referred to as MLOps – has become a major concern for organizations aiming to deploy AI at scale. The MLOps process holds great promise for accelerating AI development and demonstrating its robustness and reliability before deploying it on critical systems such as drones. The expected […]

Ce projet d’innovation vise à développer un module d’intelligence artificielle capable de détecter et classer automatiquement les émotions dans des conversations, afin d’améliorer une plateforme canadienne de simulation pour la formation en criminologie. Le ou la stagiaire concevra et validera un système qui analyse les échanges textuels entre un apprenant et des agents conversationnels (témoin, […]

This project explores how advanced artificial intelligence models can analyze network traffic while keeping sensitive data fully encrypted. Building on recent research using CryptoKAN neural networks, the project studies whether model compression techniques can make encrypted intrusion detection faster and more practical without reducing detection accuracy. The intern will first adapt and evaluate encrypted AI […]

L’épilepsie touche des millions de personnes dans le monde et se manifeste par des crises soudaines qui peuvent être difficiles à prévoir ou à détecter automatiquement. Aujourd’hui, les médecins utilisent principalement l’électroencéphalogramme (EEG) pour analyser l’activité du cerveau et identifier les signes annonciateurs de ces crises. Cependant, l’interprétation des signaux EEG est complexe et nécessite […]

This Mitacs project aims to transform hospital blood bank operations into a predictive, efficient, and data-driven system using ten years of real hospital data. Traditional blood bank systems lack analytical and forecasting capabilities, leading to shortages, overstocking, and wastage. The project will develop an end-to-end AI framework that forecasts blood demand, optimizes inventory, detects shortages […]

This project will study whether soil in the Town of The Blue Mountains is safe and suitable for urban and community farming. The Town has a long history of farming, particularly in apple production, where older agricultural practices may have left behind small amounts of chemicals or metals in the soil. With growing interest among […]

TxtSquad will develop an AI-driven intake and screening system that begins over SMS and continues in a secure web portal. Patients can upload documents and describe their symptoms, which are processed using OCR and AI to generate structured, FHIR-compatible records for EMR integration. The project will reduce administrative effort and improve intake quality for healthcare […]

Large Language Models (LLMs) are increasingly used in everyday life, yet they often make decisions based on internal assumptions and tendencies that users cannot see. These hidden patterns, called inductive biases, shape how models interpret concepts, judge situations, and respond to people. Understanding these biases is essential for building safer and more trustworthy AI systems. […]

The project is the direct continuation of application #IT47869, extending the work initiated in the first 24-week phase toward a more scalable, robust, and deployment-ready Large Language Model framework for healthcare. While the initial unit focused on designing the system architecture, implementing explainability and privacy mechanisms, and establishing preliminary evaluation methods, this second phase will […]

This project aims to help ALS improve how employees access and use information by creating an advanced AI assistant that can understand questions, search internal documents, and provide accurate answers across HR, training, and technical departments. ALS manages large amounts of complex material—such as lab procedures, policies, and technical reports—which can be difficult and time-consuming […]

This project aims to develop advanced deep learning models that combine graph-based neural networks with Kolmogorov-Arnold Networks (KAN) to improve the analysis of 3D point cloud data. These models will be applied to real-world problems such as identifying tree species from laser scanning data and studying brain connectivity from medical imaging. The collaboration will benefit […]