Description  : Physics-Informed Neural Networks (PINNs) have emerged as a paradigm for solving forward and inverse problems in computational mechanics by integrating physical laws directly into the learning process. Recently reintroduced by Raissi et al. (2019), PINNs enforce governing equations, typically partial differential equations (PDEs), as constraints in the neural network loss function, enabling the use of sparse or indirect data to infer system behavior.[...]

This PhD studentship is fully funded for 3 years, from October 2025 to September 2028.
Language: French or English only
Location: FEMTO-ST Department of Applied Mechanics, SUPMICROTECH, Université Marie et Louis Pasteur, Besançon (FR)
Deadline for application: June 15, 2025