This is a project to update the Literary and Historical Society of Quebec's permanent exhibit, Doing Time: The Quebec City Common Gaol (1808 – 1867). Since the exhibit was launched in 2011, much research has been done on the history of the gaol.  Doing Time is seen by some 25,000 visitors per year. For some, it is their only exposure to the history of prison life. The exhibit needs to be as accurate as possible. Revision will be done by an intern working under the supervision of Donald Fyson, professor of history at Université Laval and specialist in the gaol's history.

The CO2 thermal desorption process in the stripper for the regeneration of solvents is central to the design of CO2 capture process from technical and operational standpoints, requiring huge amounts of energy and posing considerable challenges on plant operation.

The goal of this research project is to study and develop optimal strategies for operating anaerobic co-digestion processes involving organic waste with cellulosic substrate derived from paper wastes. The effects of enzymatic treatment to boost degradation of the paper wastes in anaerobic conditions will also be evaluated. Furthermore, best approaches for transitioning feedstock compositions, such as from lower to higher paper content and vice versa, will be investigated.

The proposed internships aim at investigating the relevance of deep learning (DL) techniques for target detection in radar data processing. More specifically, we are looking to demonstrate the feasibility of DL techniques to deal with unusual types of data (i.e., radar data) in situations where an well performing processing with classical techniques is a challenge (e.g., detection of objects in noisy scenes from a maritime environment caused by the interference produced by the reflection of the radar waves on the sea).

Better understanding Drug-Drug interactions (DDIs) is crucial for planning therapies and drugs co-administration. While, considerable efforts are spent in labor-intensive in vivo experiments and time-consuming clinical trials, understanding the pharmacological implications and adverse side-effects for some drug combinations is challenging. The majority of interactions remains undetected until therapies are prescribed to patients. We propose to use computational tools for predicting interactions in order to reduce experimental costs and improve safety.