Vincent Adombi

A PhD position is available at Université du Québec à Chicoutimi (UQAC) in Artificial Intelligence for Water Sciences . The project sits at the intersection of computer science , artificial intelligence , and environmental science , with a focus on developing new AI methodologies to support scientific analysis and water resource management. The position is supervised by Vincent Adombi , Assistant Professor at UQAC. The successful PhD candidate will contribute to research in areas such as machine learning , deep learning , natural language processing , advanced AI systems, software development, and water sciences. The post emphasizes methodological innovation and practical applications for hydro-informatics and water-related decision support. Eligibility highlights include a completed or ongoing Master’s degree in artificial intelligence, computer science, applied mathematics, statistics, physics, engineering, or a related field. Applicants should have strong Python programming skills, a strong interest in AI method development, and demonstrate autonomy, creativity, initiative, critical thinking, and strong scientific communication skills. Funding is available for the expected duration of the doctoral program. The announcement also mentions a collaborative research environment and 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 sent directly to [email protected] . Only shortlisted candidates will be contacted for interview.

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.

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 .