PhD in Scientific Machine Learning for Hydrological Modeling Université du Québec à Chicoutimi in Canada

PhD opportunity at Université du Québec à Chicoutimi (UQAC) in Scientific Machine Learning for Hydrological Modeling, supervised by Vincent Adombi (Assistant Professor, Hydro-informatics).

The project focuses on DeepDiscover, a scientific machine learning framework aimed at uncovering the mechanisms controlling hydrological systems directly from data while respecting the fundamental laws of physics. The doctoral research sits at the intersection of artificial intelligence, applied mathematics, hydrological sciences, machine learning, deep learning, optimization, scientific computing, and physics-based modeling.

Ideal applicants should hold or be completing a Master’s degree in a relevant field such as AI, computer science, applied mathematics, statistics, physics, engineering, or a related discipline. Strong Python skills are required, along with a strong interest in developing new AI methodologies. The post also values autonomy, creativity, initiative, critical thinking, and excellent scientific communication skills. Experience in hydrology or hydrogeology is considered an asset.

Funding is available for the expected duration of the doctoral program, and the successful candidate will join a collaborative research environment with access to advanced computing resources.

To apply, candidates should prepare a CV, statement of interest, academic transcripts, and optionally a portfolio of relevant projects such as GitHub repositories, personal projects, or publications. Applications should be emailed to [email protected]. Only shortlisted candidates will be contacted for interview.