Call for a fully funded PhD position in Artificial Intelligence - SCAI, PSUAD, TotalEnergies
Topic: Deep learning for modeling physical dynamics
Deadline for applying: 30/10/2021
Location: the research will be mainly conducted at Sorbonne University of Abu Dhabi (SUAD) at UAE with regular visits to Paris, France. The diploma will be granted from the Sorbonne University of Paris, France.
Deep learning offers a new data driven approaches to the modeling of dynamical systems underlying natural observations (see Reichstein et al., 2019 for an overview on that matter). This has recently given rise to new and prolific research topics focused on exploiting deep learning methods for modeling spatio-temporal dynamics. The use of deep learning and data-driven approaches for modeling natural phenomena from physical observations however suffers from limitations such as the lack of generalization, robustness and physical plausibility. Modeling natural phenomena from physical observations indeed raises new challenges for machine learning and deep learning. Being the result of multiple interacting physical processes, the observed phenomena can be extremely complex. The data are heterogeneous, noisy, and even when plentiful they usually represent scarce and partial information about the underlying process. So, the main question which arises is: how to build accurate and fast machine learning models from this limited training data.