Xuande Chen
1 week ago
Fully Funded PhD in Coastal Infrastructure Durability and Service-Life Modeling Université du Québec à Rimouski in Canada
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position.
Deadline
Dec 28, 2026
Country
Canada
University
Universite du Quebec Institut des sciences de la mer de Rimouski

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About this position
Fully funded PhD opportunity at Université du Québec à Rimouski (UQAR), Canada. The Infrastructure Durability and Multiphysics Modeling Lab (IDMML) is recruiting a PhD student for research on coastal infrastructure durability and service-life modeling.
The project will study how real coastal microclimates affect the deterioration of reinforced concrete infrastructure. The student will work on field monitoring, sensor systems, concrete durability experiments, data analysis, and physics/data-driven modeling to characterize atmospheric, splash, tidal, and submerged exposure conditions.
The research is closely connected to the development of TransChlor Plus, a multiphysics framework for predicting chloride ingress, carbonation, corrosion, and long-term service life of concrete infrastructure.
Relevant study areas and skills: civil engineering, structural engineering, materials engineering, environmental/coastal engineering, Python, scientific computing, AI, machine learning, experimental research, and computational modeling.
Eligibility highlights: applicants with backgrounds in civil, structural, materials, environmental, coastal, or computational engineering are welcome.
Location: Rimouski, Québec, Canada.
Funding: fully funded PhD.
Start date: expected Fall 2026 or as soon as possible.
How to apply: contact the supervisor with a CV, transcripts, and a brief description of your research interests.
Funding details
Fully funded PhD position.
What's required
Applicants with backgrounds in civil, structural, materials, environmental, coastal, or computational engineering are welcome. Interest in coastal infrastructure, concrete durability, structural/environmental monitoring, Python and scientific computing, AI/machine learning, and experimental plus computational research is preferred.
How to apply
Interested candidates should contact Xuande Chen directly with a CV, transcripts, and a brief description of their research interests. Share the opportunity with potentially interested students or colleagues.
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