Whether due to urbanization or climate changes, flood events have an impact on property value. Newly available geography data about flood risk zone have yet not been utilized to their full potential in Canada. This project aims to study the impact of the flood risk zone on the value of homes for Quebec City, and to identify other regions in Canada where similar conditions could affect housing value. The development of a strategy to expand the aim of research at the country-wide level will else be part of the project.

Several studies have shown that water levels of the Great Lakes would inevitably decline in a warmer climate. These studies were based on a modeling system that was not accounting for two-way exchanges of water and energy between the atmosphere and the earth surface, hence excluding key feedback mechanisms. The general objective of this project is to improve our understanding of the Great Lakes water resources and its sensitivity to climate change.

Infertility is a health issue that affects over half a million Canadian of childbearing age. Half of these cases are related to male reproductive dysfunctions. While semen analysis provides information about some causes of male infertility, about thirty percent of these cases remains unexplained. In this context, the main goal of our project is to assess the potential of target molecules, which are secreted from the internal organs of the male reproductive system into the seminal plasma, as biomarkers for the non-invasive diagnosis of infertility.

The planned research aims to validate food allergen and gluten testing tools, and their incorporation in novel quality-management systems used in food production. It will leverage existing methodologies developed by r-Biopharm Canada (r-BPC), over the past decade through its parent company.

Rio Tinto operates aluminium plants in Saguenay that are powered by their hydroelectric system. An efficient management of water available in the system is primordial to ensure energy supply to the aluminium smelters. This quantity is uncertain since the exact inflows in the reservoirs are unknown when decisions are taken. Stochastic optimization is used to make decisions under uncertainty. Mid-term optimization models determine reservoir volumes while short-term models dispatch the available water as efficiently as possible between the power plants and turbines in the system.

Machine learning is a subfield of artificial intelligence that aims at producing computing models from observations (data), with no explicit coding made by humans. Recent advances have illustrated a strong potential of machine learning, with the potential of being a disruptive technology in many domains. For the current project, we are investigating techniques for making practical machine learning.