1. Context

Présentation of IMT Atlantique :

IMT Atlantique, internationally recognized for the quality of its research, is a leading general engineering school under the authority of the Ministry of Industry and Digital Technologies, ranked in the 3 main international rankings (THE, SHANGAI, QS). With three campuses in Brest, Nantes and Rennes, IMT Atlantique aims to combine digital technology and energy to transform society and industry through training, research and innovation.

Job environment :

The Postdoc will work in collaboration with Carlos Granero-Belinchon and Ronan Fablet from IMT Atlantique, Simon van Gennip from Mercator Ocean International, and Bertrand Chapron from Ifremer. Thus, the research team is composed by physicist, oceanographers and artificial intelligence researchers from different laboratories, leading to a multidisciplinary project. Moreover, the postdocwill develop within the OSE research team at IMT (https://cia-oceanix.github.io/) which is a dynamic research group on image processing and artificial intelligence for Oceanography and Climate. The postdoc will also be part of the new Inria team Odissey (https://team.inria.fr/odyssey/)

2. Objective

Thus, this project aims to reconstruct the unknown states of the ocean surface from physical knowledge of the system and available data that can be spatially distant, prior in time, at coarser resolution etc. We can then envisage physics-informed super-resolution, data generation and forecasting among other applications.

The main methodological objective is the formulation of multiscale DL models able to extract non-linear couplings. Moreover, we want this models to 1) be based on the physics of the system, and so to have a physics guided learning, and 2) to be interpretable from a physics point of view. With this purpose both the loss function and the model architectures will be adapted. In order for our model to be a emulator of the state of the ocean, and then to take into account its turbulent nature, a stochastic component will be included and the incertitudes of the reconstructed states quantified.

3. Training and skills

Training

• PhD less than 3 years old in Deep Learning/Machine learning or Oceanography. Ideally combining both fields.

Knowledge & Skills

• Neural Network models, Fluid Physics/Oceanography, Python, Pytorch
• Teamwork
•  Multidisciplinary research

The candidate must have passed at least 18 months in a non-French laboratory between May 1, 2019 and the start of the project.

Benefits

• 49 days of annual leave and time off
• Partial telecommuting possible
• Public transport paid for
• Sustainable mobility package (for carpooling or cycling)
• Family supplement
• Wide range of social benefits

Additional information and application

• Application deadline : 16/02/2023
• Start of contract : As soon as possible
  
• Type of contract : Fixed-term contract under public law
• Duration of the contract : 24 months,
  
• Geographic location : IMT Atlantique - 655 Av. du Technopôle, 29280 Plouzané
  
• Project : SAD MODELS
• The positions offered for recruitment are open to all with, upon request, accommodations for candidates with disabilities
• Hierarchical Category: Category II trade P of the LMI management cadre
• For more information, please contact : carlos.granero-belinchon@imt-atlantique.fr
  
  

Application documents

• Resume with list of publications
• Cover letter
• Names and emails of referees that may be contacted