Emerging contaminants (ECs) have become a significant environmental concern due to their widespread presence and risks to health and ecosystems. Meanwhile, agricultural waste is also increasing, providing a valuable source for high-value materials. Traditional detection methods are costly, slow, and require specialized equipment and time-consuming laboratory analyses. Electrochemical sensors offer a low-cost, rapid, and sensitive […]

Organohalide-respiring bacteria (OHRB) are important agents of bioremediation, helping to clean up groundwater contaminated with toxic compounds such as chloroform, perchloroethene (PCE), and chlorobenzene. This metabolic pathway relies on enzymes known as reductive dehalogenase homologs (RdhAs). The mechanisms by which RdhAs reduce organohalides remains elusive, particularly in the case of the Chloroflexi OHRB Dehalococcoides and […]

Ce projet de stage vise à étudier et à valider le potentiel commercial d’un procédé innovant de séquestration du dioxyde de carbone (CO2) par carbonatation minérale accélérée in situ des résidus miniers issus de roches ultramafiques (ou ultrabasiques). Il s’agit d’une solution prometteuse pour réduire l’impact des émissions de CO2 par les différentes industries. Ce […]

This project focuses on investigating more efficient and simplified approaches for very short-term precipitation forecasting, known as nowcasting. The goal is to evaluate and adapt lightweight machine learning models that can deliver accurate predictions using fewer computational resources. By reviewing existing models, testing their performance, and exploring ways to simplify their architecture without losing accuracy, […]

Many rural communities in the maritime Provinces rely on groundwater for drinking water supply. There is concern that the groundwater in these areas may become contaminated with naturally occurring arsenic as the climate changes. Climate change effects including rising sea levels, longer summer droughts, and more intense storms may alter the geochemical conditions in the […]

Ce projet répond à l’enjeu mondial urgent de la durabilité des eaux souterraines face aux changements climatiques, en développant un cadre de modélisation robuste, alimenté par l’intelligence artificielle, pour évaluer et comparer la recharge et la vulnérabilité des nappes phréatiques dans deux régions aux climats contrastés : la plaine semi-aride de Berrechid au Maroc et […]

Climate change is one of the main concerns of our society and is closely linked to the large consumption of fossil fuels and their associated carbon emissions. An appealing alternative is the production of hydrogen from water, powered by sunlight. Our project aims to develop a first family of efficient hybrid dyadic catalysts for this […]

ESSA Technologies Ltd. (ESSA) is a Canadian environmental consulting company focused on supporting sustainable ecosystems and communities through science and analytical tools. With offices in Vancouver and Ottawa, ESSA collaborates with clients to address environmental challenges through data-driven, evidence-based decision-making processes. The innovation challenge facing ESSA is the need for more efficient internal processes and […]

This project uses AI and smart thermostats to predict heatwaves in Canada, helping protect public health. By analyzing indoor and outdoor temperature data, we aim to improve heatwave forecasting and understand how extreme heat affects indoor spaces. Using machine learning, we develop models to predict future indoor temperatures, supporting better planning and response strategies. The […]

Floating photovoltaics (FPVs), an emerging technology used to harvest solar energy from the surface of waterbodies, has grown rapidly in recent years with the increase in initiatives to decarbonize energy production. However, much of the literature on environmental impacts of FPVs focuses on water quality rather than on impacts on biotic processes such as primary […]

This proposal aims to integrate mathematical analysis with numerical simulations using Physics-Informed Neural Network (PINN) schemes to investigate the primitive equations, a fundamental model for atmospheric and geophysical flows. The primary focus is on achieving a precise characterization of singularity formation in these models.

This project aims to improve the monitoring and management of soil erosion in agricultural landscapes using advanced technologies like combinations of optical and LiDAR data from remotely piloted aircraft with machine learning. By creating detailed, high-resolution models of soil erosion and deposition, the project will help quantify erosion processes more efficiently than traditional methods. The […]