Offre en lien avec l’Action/le Réseau : MACLEAN/– — –
Laboratoire/Entreprise : Lab-STICC/IMT Atlantique
Durée : 6 months
Contact : ronan.fablet@imt-atlantique.fr
Date limite de publication : 2023-01-24
Contexte :
We open internship offers in the framework of AI chair OceaniX (https://cia-oceanix.github.io/) to develop Physics-Informed AI for Ocean Monitoring and Surveillance.
Contact: Ronan Fablet, ronan.fablet@imt-atlantique.fr
Sujet :
Data-driven and learning-based strategies for the analysis, modeling and reconstruction of dynamical systems are currently emerging as promising research directions as an alternative to classic model-driven approaches for a wide variety of application fields, including atmosphere and ocean science, remote sensing, computer vision…. [2,3,4]. Especially, deep learning schemes [1] are currently investigated to address inverse problems, i.e. reconstruction of signals or images from observations. Especially, recent works [e.g., 3,4] have shown that one can learn variational models and solvers for the reconstruction.
These internships will specifically investigate the development of deep learning inverse models for the space-time reconstruction of geophysical dynamics from partial observations. We aim to explore and understand how end-to-end neural schemes, such as 4DVarNets [3,5], provide new means to address limitations of operational data assimilation systems, especially for applications to ocean modeling and forecasting using satellite and in situ observations. Both simulated and real case-studies will be of interest.
Keywords: deep learning, inverse problems, data assimilation, space oceanography
Profil du candidat :
MSc. and/or engineer degree in Applied Math., Data Science and/or Computer Science with a strong theoretical background, proven programming skills (Python).
Formation et compétences requises :
Knowledge of deep learning models and a first experience with Pytorch would be a plus.
Adresse d’emploi :
IMT Atlantique, Brest
Document attaché : 202211240841_sujet_stage_4DVarNet_DA2022.pdf