L2M QC 2024 – « PAX »

Tout comme les grandes entreprises, les PME rencontrent des problématiques importantes quant à la sélection et le développement de leur personnel. Contrairement aux grandes entreprises, les PME n’ont souvent pas les ressources nécessaires pour accéder à des services professionnels et des outils de qualité. C’est la raison pour laquelle je développe un nouveau service abordable […]

Read More
L2M QC 2024 – « Real-time optical processor for next-generation telecom interconnect.»

Over the past decade, the telecommunication sector progressed and evolved quickly due to the installation of cloud-based artificial intelligence, which now needs at least data rates of 1.6 Tb/s and beyond to meet global demand. These changes are pushing the industry towards deploying scalable, highly performing, and low-energy-consuming computing units in telecom interconnect infrastructure. This […]

Read More
L2M QC 2024 – « Infonuage télé-robotique : validation du besoin commercial des robots téléopérés dans les infrastructures en régions nordiques ou éloignées »

Le produit et les services qui sont étudiés dans l’analyse de marché visent à résoudre le problème de l’accessibilité et de l’efficacité des interventions dans les sous-stations électriques situées dans des zones éloignées et difficiles d’accès, en particulier dans le grand nord canadien. Ce stage favorisera l’étude du marché et la validation de la solution […]

Read More
Functional MRI for post-stroke rehabilitation

Motor impairment is a common symptom after stroke and recovery of motor function is important for regaining the patient’s independence in activities of daily living. Being able to predict motion recovery and outcomes soon after stroke could support clinicians, patients and families to set proper goals for rehabilitation and appropriate plans of time and resource […]

Read More
L2M – Reinforcement-Learning-Driven Electronic Design Automation (EDA) for Optimal Layout Placement

Reinforcement Learning (RL) driven Electronic Design Automation (EDA) is revolutionizing layout placement optimization for integrated circuits, enabling a faster design process. By incorporating the RL techniques, we enhance the historically precise yet labor-intensive process for smart integrated circuit (IC) fabrication. This innovative approach streamlines IC design, accounting for factors such as foggy and proximity effects, […]

Read More
L2M – Collabrix AI: Bridging the Gap Between Academic Talent and Startup Needs

Collabrix AI is developing a platform to connect startups and SMEs with student teams for project execution. By using an AI algorithm to match projects with the right student teams, Collabrix AI helps businesses access flexible, affordable talent while providing students with valuable real-world experience. This project aims to validate the market need for such […]

Read More
L2M – Enabling knowledge transfer between science education and coastal communities by leveraging generative AI and climate science publications

There are over 250 million scientific publications and reports with an increasing rate published each year, yet many are not accessible to the public due to their technical language and content hidden behind paywalls. This project aims to leverage AI (Artificial Intelligence) and a curated database of ocean-climate literature to enable educators and students to […]

Read More
L2M – Satellite Monitoring, Analysis, and Reporting Tool for Harmful Algae Bloom identification: introducing SMART-HAB, a machine-learning tool to identify and visualize harmful algae blooms in near-real time.

Harmful algal blooms (HABs) are a growing threat to drinking water, fisheries, public health, and recreation. In recent years, HABs have increased in frequency and severity in both freshwater and marine environments. Blooms are hard to monitor because they can occur unexpectedly, and reporting methods across Canada are inconsistent, creating a patchwork of alerting methods […]

Read More
Remote Video Surveillance using LEO Satellites Communications

Video surveillance systems have rapidly expanded, driven by their critical roles in security and traffic monitoring. This expansion has produced vast data volumes, causing bottlenecks in communication systems due to the time-sensitive and bandwidth-intensive nature of surveillance data. To address these challenges, research has increasingly focused on developing algorithms to compress redundant data effectively, evolving […]

Read More
Cost-Effective and Intelligent Monitoring of Large-Scale Software Systems

This research project aims to tackle the challenge of ensuring the reliability of large-scale software systems, which are crucial for various applications such as online services and data processing. The project focuses on developing cost-effective and intelligent methods for monitoring these systems to anticipate and prevent runtime incidents, such as crashes or errors, which can […]

Read More
Reinforcement Learning for Micro-Grid Control and Optimization

This project aims at optimizing the use of energy in micro-grids. Reinforcement learning, a branch of artificial intelligence, will be used to reduce the consumption of fossil fuel by better deciding when to charge or discharge batteries. Using reinforcement learning for this automated decision making problem could be interesting as it should allow to handle […]

Read More