Offre en lien avec l’Action/le Réseau : MACLEAN/– — –

Laboratoire/Entreprise : Lab-STICC/IMT Atlantique
Durée : 6 mois
Contact : ronan.fablet@imt-atlantique.fr
Date limite de publication : 2020-01-15

Contexte :
This internship offer is open in the framework of AI chair OceaniX (https://cia-oceanix.github.io/), which develops Physics-Informed AI for Ocean Monitoring and Surveillance. PhD opportunities are likely to be offered for the successful candidate.

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.
This internship will specifically investigate the development of deep learning inverse models for SAR imaging to retrieve sea surface geophysical parameters (e.g., wave fields, sea surface winds,..). Based on a review of the state-of-the-art [eg, 3,4,5], the goal will to propose, implement and evaluate different deep learning schemes. Case-studies with groundtruthed real SAR imaging datasets will be considered and implemented.
Keywords: deep learning, inverse problems, variational models, SAR imaging; ocean remote sensing.

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 :
Advanced knowledge of deep learning models and a first experience with Pytorch would be a plus.

Adresse d’emploi :
IMT atlantique, Brest

Document attaché : 202012171704_sujet_stage_DLSARImaging_2021.pdf