Physics-Aware Deep Learning for Modeling Spatio-Temporal Dynamics.
Contact : Patrick Gallinari, patrick.gallinari@sorbonne-universite.fr
Location: Sorbonne Université, Pierre et Marie Curie Campus, 4 Place Jussieu, Paris, Fr
Candidate profile: Master degree in computer science or applied mathematics, Engineering school. Background and experience in machine learning. Good technical skills in programming.
How to apply: please send a cv, motivation letter, grades obtained in master, recommendation letters when possible to patrick.gallinari@sorbonne-universite.fr
Start date: October/November 2023
Note: The research topic is open and depending on the candidate profile could be oriented more on the theory or on the application side
Keywords: deep learning, physics-aware deep learning, climate data, fluid dynamics, earth science
Full description: https://pages.isir.upmc.fr/gallinari/open-positions/Abstract: Physics-aware deep learning is an emerging research field aiming at investigating the potential of AI methods to advance scientific research for the modeling of complex natural phenomena. This is a fast-growing field with the potential to boost scientific progress and to change the way we develop research in a whole range of scientific domains. An area where this idea raises high hopes is the modeling of complex dynamics characterizing natural phenomena occurring in domains as diverse as climate science, earth science, biology, fluid dynamics, etc. Although promising and rapidly developing, this research field faces several challenges. For this PhD project we will address two main challenges, namely the construction of hybrid models for integrating physics with DL and generalization issues which condition the usability of DL for physics.