Innovative Projects Realized

Caractérisation d’une torche plasma à la pression atmosphérique pour le dépôt de couches minces

Ce projet porte sur la caractérisation d’une torche plasma à arc soufflé à la pression atmosphérique pour le dépôt de couches minces organosiliciées. La caractérisation se concentre principalement sur l’interaction précurseur plasma et et son impact sur la physicochimie des couches déposées. La principale méthode de caractérisation du plasma sera faite à l’aide de l’utilisation d’un analyseur hyper-spectrale permettant une acquisition spatiales des propriétés optiques de la décharge. Des mesures de caractérisations seront également effectuées en parallèles.

Stage de recherche en ingénierie des systèmes cognitifs appliquée à la cyberjustice

Mon projet de recherche vise à rendre la justice numérique plus accessible et humaine en adaptant les outils technologiques aux capacités de compréhension des citoyens. Dans le cadre de ce stage au Laboratoire de cyberjustice, j’interviens en tant qu’élève-ingénieure en cognitique pour analyser comment les utilisateurs interagissent avec les systèmes judiciaires automatisés. Mon objectif est d’identifier les obstacles à la compréhension et de proposer des interfaces simplifiées qui réduisent la charge mentale des justiciables.

Le temps en droit de la filiation sous le prisme des assistances à la procréation

La naissance de Louise Brown en 1978 n’a pas seulement marqué la venue au monde du premier bébé éprouvette, elle a également marqué le début d’une révolution procréative permise par le succès de la fécondation in vitro. Si ces progrès ont rapidement été plébiscités par la société, leur réalisation a cependant révélé l’inadéquation du droit à reconnaître le lien de filiation de l’enfant issu d’une assistance à la procréation impliquant un tiers. C’est donc au gré des contentieux que les juges ont façonné leur jurisprudence suppléant ainsi le silence des textes. Les problématiques juridiques soulevées par ces pratiques demeurent importantes et les affaires qui en découlent font fréquemment l’objet d’une médiatisation trouvant un fort écho dans la société. Parce que ces enjeux touchent à la famille et donc à l’intimité des personnes, le droit de la filiation se doit de parvenir à une réponse respectueuse des intérêts en présence. L’objectif de la recherche est alors d’étudier les droits québécois et français pour analyser leur approche de la filiation de l’enfant né d’une assistance à la procréation, et de réfléchir aux propositions qui pourraient être formulées pour remédier aux insécurités juridiques dont sont sources les normes existantes à ce jour.

ESROP – Exploring Metallization of Tandem Solar Cells through Machine Learning and Simulations

Tandem solar cells represent the future of renewable solar energy, offering efficiencies beyond traditional single-junction silicon cells. However, their current commercial viability is limited by their metallization pattern, which is the silver contacts on the surface that collect electricity. Current design processes are manual and computationally expensive; therefore, this project aims to integrate machine learning to optimize the design of these patterns. By developing an algorithm in Python and Griddler software, we aim to optimize both the power conversion efficiency and production costs of the tandem solar cells, accelerating the global transition to clean, renewable solar technology.

ESROP – Physics-Informed Neural Network Engine for Perovskite Solar Cells

Perovskite solar cells are a promising next-generation energy technology, yet their widespread commercial adoption is hindered by extreme sensitivity to manufacturing conditions, leading to unpredictable performance. This project aims to overcome this reliability challenge by developing a “smart” predictive engine that integrates artificial intelligence with fundamental physics. By leveraging a massive dataset of nearly 500,000 device measurements, interns will construct a modular system that automatically categorizes solar cells into distinct performance regimes and trains specialized “physics-Informed” models for each category. This innovative approach ensures that AI predictions remain consistent with physical laws, offering a significant improvement over traditional tools that struggle with complex hardware data. The project benefits the National University of Singapore by delivering a robust, automated tool to accelerate the optimization of solar materials, while enabling the University of Toronto to deepen its capacity in Scientific Machine Learning, fostering international knowledge transfer that supports the global transition to clean energy.

Optical Polarization Observations of Blazars and Tidal Disruption Events

This project aims to understand the processes of accretion disk and jet formation around supermassive black hole systems. It will be performed within the Black hOle Optical-polarization TimE-domain Survey (BOOTES, https://www.ia.forth.gr/index.php/project/1055), designed to study the polarized light coming from persistent and transient black hole systems called blazars and Tidal Disruption Events (TDEs), respectively. Within the framework of BOOTES, the intern will be working on the acquisition, and data analysis of optical polarization observations using a novel high-accuracy polarimeter mounted at the 1.3-metre telescope of the Skinakas Observatory called RoboPol. The project will allow the intern to acquire hands-on experience in cutting-edge astrophysical research as well as the operation of world-class telescopes by joining the BOOTES team at the Institute of Astrophysics in Crete to study some of the universe’s most extreme objects. The proposed collaboration strengthens research ties between the participating institutions, supports student training in advanced observational techniques, and contributes valuable data to an international effort to study high-energy astrophysical phenomena.

ESROP – Time-Series Data Modelling and Analysis with a Focus on Pattern Analysis

The research addresses the fundamental trade-off in financial econometrics between computational efficiency and predictive accuracy under varying market conditions. While existing literature has established hybrid models with high predictive capabilities, these often incur high computational costs that may not be necessary during stable market regimes. Conversely, traditional regime-switching models like the MS-GARCH utilize a Markov switch to adjust parameters but often lack the predictive depth required for unstable periods.

This project aims to bridge the gap by developing a mechanism that implements Markov switches between GARCH and LSTM architectures based on the detected regime.

Applying Machine Learning Methods to Sustainable Mining

This project aims to support the development of low-carbon materials for mining applications by using data-driven methods to better understand and predict material durability. The research focuses on analyzing existing experimental data to estimate carbonation depth in alternative concrete made from mine tailings, which can reduce the use of traditional cement and lower greenhouse gas emissions. By combining engineering knowledge with machine learning tools, the project will help improve confidence in the use of sustainable construction materials in the mining sector. The internship will strengthen research collaboration between Université Laval and an international partner institution, while providing valuable training opportunities for a highly motivated undergraduate student in applied data analysis and sustainable engineering.

Stage de recherche de 4 mois – Département des Sciences Biologiques — Biais mâles-femelles des souris scd1 knock-out au foie en réponse à un régime riche en graisses et en sucre

Ce projet de recherche utilise un modèle de souris pour mieux comprendre comment le foie gère les lipides et pourquoi les femmes et les hommes réagissent différemment face à l’accumulation de gras et aux maladies métaboliques. En modifiant une enzyme clé impliquée dans la fabrication des lipides au niveau du foie et en exposant les animaux à différents types d’alimentation, l’étude permet d’observer comment le métabolisme change, comment les lipides sont stockés ou transportés dans le corps et comment ces réponses varient selon le sexe. Les analyses portent notamment sur l’accumulation de gras dans le foie, la composition des lipides dans le sang et l’utilisation de l’énergie par l’organisme. L’objectif est de mieux comprendre les mécanismes biologiques responsables des différences entre les femmes et les hommes afin de contribuer, à long terme, au développement de stratégies de prévention et de traitements mieux adaptés aux réalités de chacun.

Coherent Elastic Neutrino-Nucleus Scattering Measurement with the Ricochet Experiment

This Mitacs Globalink Project will place a University of Toronto undergraduate physics student at the experimental site of a high-flux reactor-based neutrino experiment called RICOCHET in Grenoble, France. The intern will be hosted at Université Grenoble Alpes and will work closely with the local research team to support experiment operations and to develop a quantitative estimate of the experiment’s sensitivity. This work is crucial to the experiment’s success, as it helps guide how data are collected and interpreted on the path toward RICOCHET’s physics results. It will also build local expertise at the University of Toronto in reactor-site experimental practice and advanced data analysis, which the intern will bring back through continued research contributions and knowledge sharing.

ESROP Physics-Informed Neural Network Engine for Perovskite Solar Cells

Perovskite solar cells are a promising new solar technology that could enable cheaper and more efficient renewable energy. However, their performance can vary widely because even small changes during manufacturing can strongly affect how well the devices work. This project uses advanced artificial intelligence techniques, combined with known physical principles, to analyze a large collection of solar cell performance data. By identifying patterns in how different devices behave and tailoring models to these patterns, the project aims to create a more reliable way to predict and understand perovskite solar cell performance. Ultimately, this work will help support the development of more consistent, scalable, and commercially viable solar energy technologies.

ESROP + Implementation of mini PV module measurement system for outdoor testbeds

This research project aims to create a vertical outdoor testbed for small-scale building integrated photovoltaics (BIPV) modules to enable high-resolution performance monitoring.