Innovative Projects Realized

Quantum Simulations with Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving PDEs, fractional equations, and eigenvalue problems, making them particularly suited for quantum mechanics, where traditional numerical methods often face computational bottlenecks. The aim of this internship is to analyse the applicability of PINNs to a broad range of quantum simulation tasks such as solving the Schrödinger equation for multi-electron systems or simulating molecular dynamics for processes like silicidation. The intern will engage in hands-on scientific machine learning, contribute to improvements in quantum simulation, and acquire valuable skills in a rapidly evolving field.

Light collection optimization with semiconductor photodetectors – Year two

Better light collection by photodetectors would automatically improve signal to noise ratio and photodetector performance. For example, for the positron emission tomography application, it would result in a better image quality for a lower dose. The main cause of the light loss is too much reflection of light that is trying to travel from the light source into the semiconductor light sensor. Several options are possible to overcome this difficulty and will be investigated, such as anti-reflective coatings, the use of an optical coupling compound with a higher refractive index to
bound the medium and the photodetector, or micro and nano structures on both the medium and the photodetector. The research will be aimed at finding the best compromise between light collection optimization and feasibility, to improve performance of photodetectors designed and manufactured by Excelitas Canada.

Light collection optimization with semiconductor photodetectors

Better light collection by photodetectors would automatically improve signal to noise ratio and photodetector performance. For example, for the positron emission tomography application, it would result in a better image quality for a lower dose. The main cause of the light loss is too much reflection of light that is trying to travel from the light source into the semiconductor light sensor. Several options are possible to overcome this difficulty and will be investigated, such as anti-reflective coatings, the use of an optical coupling compound with a higher refractive index to
bound the medium and the photodetector, or micro and nano structures on both the medium and the photodetector. The research will be aimed at finding the best compromise between light collection optimization and feasibility, to improve performance of photodetectors designed and manufactured by Excelitas Canada.

Optimization of Super Heavy Lift Drone Design utilizing Computational Fluid Dynamics and a Design of Experiments approach

Heavy-lift drones offer promising solutions for challenges in wildfire response, construction, and logistics, thanks to their rapid deployment and lower environmental impact. As Pulshn focuses on developing drones capable of transporting heavy payloads, this research aims to support the optimization of drone design for enhanced performance and reliability. Specifically, the study will explore the aerodynamic behavior of ducted fan configurations to improve mass flow rate and thrust efficiency. Computational Fluid Dynamics (CFD) will be used to evaluate aerodynamic performance, while a Design of Experiments (DOE) approach will assess how key design parameters influence outcomes. The findings will guide the development of a next-generation heavy-lift drone capable of carrying up to 1,000 kilograms. This advancement aligns with Pulshn’s goals and could significantly expand commercial applications in emergency response, infrastructure development, and cargo transport.

Policy Analysis for Inclusive Cooperative Growth: Strengthening Expertise Hub Cooperative’s Capacity to Support Immigrant Communities

Expertise Hub Cooperative (EHC) seeks to engage a policy analyst to strengthen its ability to navigate evolving policies and support its immigrant-focused mission. This initiative will provide actionable insights, policy recommendations, and strategies to address systemic barriers, advocate for change, and enhance EHC’s capacity to foster inclusivity and community impact. By aligning with EHC’s cooperative values, this work will empower the organization to adapt to policy changes and better support its members and programs. This will support Expertise Hub Cooperative (EHC) in navigating the evolving policy landscape that impacts the cooperative and its immigrant-focused members. By delivering comprehensive policy analysis, stakeholder insights, and actionable recommendations, this project will empower EHC to address systemic barriers, foster inclusivity, and advance its mission of building a connected and empowered immigrant community.

EHC’s work goes beyond workforce development, fostering connections through workshops, job fairs, and events that empower immigrants to thrive socially, culturally, and economically. This initiative will provide EHC with the tools and insights needed to adapt to current policies and trends while strengthening its impact.

Electrifying Bike Sharing Systems: Structural Demand Estimation and Large-scale Optimization

Bikeshare systems around the world are increasingly incorporating electric bikes to expand their coverage and increase ridership. This project aims to apply descriptive and prescriptive analytics to support key operational and strategic decisions in bikeshare electrification. First, we will perform empirical analysis to quantify the spatially heterogeneous impact of electric bike availability on bikeshare ridership. Then, we will develop an optimization model that jointly determines charging infrastructure locations and battery swapping policies—two of the most common charging strategies in e-bike sharing systems—to maximize overall ridership. Together, these efforts will offer a comprehensive, data-driven approach to electrifying bikeshare systems and promote the broader adoption of this sustainable mode of transportation.

Le Music Legacy Project – Bibliothèque des performances musicales du monde en Réalité Virtuelle

Conscient des difficultés auxquelles font face de nombreux musiciens, ingénieurs du son et réalisateurs dans différents pays (et convaincu de l’importance de l’engagement du chercheur auprès des populations étudiées), je souhaite développer à travers mes recherches postdoctorales une structure qui fasse le pont entre recherche ethnomusicologique, création musicale et engagement social. Pour ce faire, il s’agit de réaliser une série d’enregistrements musicaux à travers le monde, à l’aide d’un studio nomade, avant de les travailler en laboratoire (au LARC, à l’Université Laval), pour ensuite les diffuser via des réseaux commerciaux établis par l’entreprise La Hacienda Creative, spécialisée dans la post-production audionumérique. Dans cette perspective, l’objectif principal de ce projet postdoctoral est de créer une plateforme de recherchecréation
visant à décloisonner l’expérience de recherche ethnomusicologique. Cette structure servira de cadre expérimental utile au renforcement des relations de réciprocité entre chercheurs, musiciens et réalisateurs provenant des différents pays visités.

The Biomechanical Differences between the SideStix? Crutch and a Traditional Forearm Crutch

Many people with physical disabilities depend on forearm crutches to help them walk. Although forearm crutches are helpful, many people experience overuse injuries as a result of using the forearm crutch. Injuries to the wrists, elbows and shoulders are commonly experienced with long-term use of the forearm crutch. The research project will examine differences between the traditional forearm crutch and a newly developed SideStix? crutch. The SideStix?crutch has been developed with a spring-like device and rotating footpads. It is hypothesized that the new crutch will effectively change body movement while crutch walking while reducing the impact on the body. SideStix? Ventures Inc. will greatly benefit from this project by understanding how their crutch influences walking and it may reduce overuse injuries.

Improving the Detection of Performance Regressions with Change Point Detection Methods

As software systems evolve, the software performance has to be constantly monitored to ensure that systems are responsive and economically sustainable. Companies like Mozilla have dedicated teams and an automated workflow to detect performance regression. However, due to the nature of performance measurements, the automatic detection of regressions is bound to flag issues falsely, which makes the performance team waste investigation time, and miss actual regressions, which could impact end users.

This research project aims to explore the state-of-the-art methods in change point detection (CPD) to improve the accuracy of Mozilla’s performance detection system. We plan to create a real-world dataset of validated performance alerts, explore the efficacy of multiple CPD methods on Mozilla’s data, and integrate the best techniques in Mozilla’s detection system. A more effective performance regression detection system would improve team productivity, enabling them to focus their efforts on fixing real performance anomalies, ultimately making the company’s products better and more responsive.

Developing open source user tools for Planet’s hyperspectral remote sensing data

This project will help make it easier for scientists and data engineers to work with a powerful type of satellite data called hyperspectral imagery, which captures detailed information about the Earth’s surface. While this data can support important work in areas like climate, agriculture, and environmental monitoring, it’s often difficult to use without specialized training or tools. The intern will evaluate a range of existing open-source software tools used to process and analyze hyperspectral data, comparing their performance, ease of use, and technical features. This research will identify common challenges and recommend improvements that can make these tools more accessible to a wider community of users.
The project directly supports Planet’s efforts to improve access to data from its new hyperspectral satellite, Tanager-1, by guiding the development of a user-friendly, open-source Python library. The intern’s work will help Planet expand its customer base, support applied research, and contribute to broader goals in open science and environmental technology innovation.

Évaluation longitudinale des impacts d’essais de vélo à assistance électrique (VAE)

(1) Équiterre est une ONG canadienne opérant au Québec. Sa mission est de promouvoir des objectifs écologiques, équitables et sociaux. Elle mène de nombreuses activités pour promouvoir ces objectifs auprès des citoyens, des organisations et des gouvernements. Équiterre a plus de 25 ans d’expérience dans la sensibilisation et la mobilisation pour le développement durable. Le programme « Vélovolt » s’inscrit dans ce plan stratégique avec une vision à long terme du transport durable.
(2) Dans le cadre du programme Vélovolt (Phase 2 – 2025-2027), Équiterre mène une campagne d’essais de vélos à assistance électrique (VAE) à travers le Québec afin de favoriser un transfert modal de l’automobile vers le transport actif, réduire les émissions de GES et améliorer la santé publique. Le programme permet à des employés des organisations participantes de faire des essais de VAE en libre-service (< 48h) et/ou de longue durée (1 à 4 semaines). Ce projet Mitacs vise à offrir une assistance méthodologique à Équiterre afin de réaliser un rigoureux processus d’évaluation des impacts de ces essais sur les participants et les organisations. (3) Le transport est un secteur majeur qui doit être ciblé pour atteindre les objectifs de réduction de GES du Québec et du Canada. L’adoption des VAE comme mode de transport alternatif à la voiture privée demeure marginale. Le projet permettra à Équiterre de récolter des données probantes sur les impacts de la campagne d’essais et sur le potentiel d’adoption du VAE dans divers contextes afin de formuler des recommandations de politiques publiques ambitieuses pour soutenir la réduction de GES dans le secteur des transports.

An Accessible AI Companion for Older Adults and Caregivers

Garrison?Care in partnership with Sheridan College’s Centre for Applied AI (CAAI) will develop Ask?SarAi, an AI powered tech support and home care companion that delivers plain language, Accessibility for Ontarians with Disabilities Act (AODA) compliant guidance through a web based conversational interface. Ask?SarAi helps older adults independently manage smart home and health devices, such as setting up blood pressure monitors, using voice assistants, and detecting potential scams, while safeguarding privacy under a Privacy by Design model. Co designed with caregivers, Personal Support Workers (PSWs), social workers, and older users, it combines a structured knowledge base, a high fidelity prototype, and a co created training manual to establish best practices in accessible conversational design, modular information architecture, and cognitive load sensitive interfaces. The deliverables include an AODA aligned design system, usability feedback report, style guide, and research white paper aimed at empowering end users, reduce caregiver burden, and generate replicable insights for scalable integration into both institutional and home care settings.