Postdoctoral Fellow in Groundwater Level Modeling, AI, and Hydrological Modeling at Université du Québec à Chicoutimi Université du Québec à Chicoutimi in Canada

Université du Québec à Chicoutimi (UQAC) is offering a postdoctoral fellowship in groundwater level modeling, artificial intelligence, hydrological modeling, time series analysis, uncertainty quantification, and model explainability.

The project is carried out in partnership with the Ministry of the Environment (MELCCFP) and focuses on developing a state-of-the-art methodology for modeling groundwater levels using data from the Quebec Groundwater Monitoring Network (QSWN). The work combines machine learning, deep learning, hybrid modeling, transfer learning, and physics-aware approaches to improve prediction and interpretability across hydrogeological contexts.

The successful candidate will work under the supervision of Assistant Professor Vincent Adombi at UQAC in Chicoutimi, Quebec, Canada. Responsibilities include processing hydroclimatic data, developing and comparing machine/deep/hybrid models, building transfer learning approaches, improving physical consistency of models, quantifying uncertainty, producing Python code and reproducible tools, and contributing to reports, presentations, and scientific publications.

Eligibility highlights: PhD obtained or in progress in AI, data science, hydrology, hydrogeology, or a related field; experience in machine and deep learning; strong Python programming skills (Scikit-Learn, PyTorch/TensorFlow, NumPy, Pandas, etc.); experience with time series analysis; ability to work independently; relevant publications; and excellent writing skills. Preferred assets include hydrogeology/hydrology, signal processing, conceptual hydrological modelling, scientific machine learning, hydrological uncertainty quantification, explainable AI, transfer learning, and transformer or graph neural network architectures.

Conditions: 18-month duration, start in Fall 2026, regular presence at UQAC, and competitive salary based on experience.

How to apply: Send a CV with publications and a cover letter to [email protected]. The contact details of referees may be requested from shortlisted candidates. Deadline: 2026-06-30.