• August 2022: paper accepted in Geoscientific Model Development, “Analog Data Assimilation for the Selection of Suitable General Circulation Models“, by Ruiz et al., more details here
  • August 2022: paper accepted in Quarterly Journal of the Royal Meteorological Society, “Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification“, by Sacco et al., more details here
  • August 2022: paper published in Remote Sensing Letters, “Two-dimensional structure functions for characterizing convective rolls in the marine atmospheric boundary layer from Sentinel-1 SAR images“, by Granero Belinchon et al., more details here
  • July 2022: paper accepted in Ocean Science, “Four-dimensional temperature, salinity and mixed layer depth in the Gulf Stream, reconstructed from remote sensing and in situ observations with neural networks“, by Pauthenet et al., more details here
  • June 2022: new preprint in Wind Energy Science, “Gaussian Mixture Models for the Optimal Sparse Sampling of Offshore Wind Resource“, by Marcille et al., more details here


  • Since 2022: member of the Odyssey research team (with INRIA and IFREMER)
  • Since 2022: editor of EGU journal Nonlinear Processes in Geophysics
  • Since 2021: head of the Ocean Data Science Master program, with IUEM, ENSTA Bretagne, and IMT Atlantique
  • Since 2021: head of the AI & Ocean program at Lab-STICC, UMR CNRS 6285
  • Since 2019: associate researcher at the Data Assimilation Research Team, RIKEN Center for Computational Science (Kobe, Japan)
  • Since 2015: associate professor at IMT Atlantique (Brest, France) and researcher at lab-STICC (French CNRS laboratory)
  • 2012-2015: postdoctoral researcher on spatial oceanography and statistics at Télécom Bretagne (Brest, France)
  • 2010-2012: postdoctoral researcher in data assimilation at the Atmospheric Science Research Group, Univ. Corrientes (Argentina)
  • 2007-2010: PhD at the Oceanography from Space Laboratory of IFREMER (Brest, France)

Research topics:

— Data assimilation:

— Remote Sensing:

— Climate change, renewable energies and risk assessment:

Teaching activities:

  • Probability and statistics (40h/year)
  • Machine learning and deep learning (20h/year)
  • Big data and cloud computing (30h/year)
  • Data assimilation (30h/year)


  • SALMO-Skol (summer school)
    2022, Sizun, France, more details here
  • Ocean Remote Sensing Synergy (summer school)
    2014-2015-2016-2018-2019, Brest, France, more details here
  • Data Science & Environment (workshop + summer school)
    2017, Brest, France, more details here
  • Statistical Modeling and Machine Learning in Meteorology and Oceanography (workshop)
    2019-2020-2021, Brest, France, more details here
  • Data Science for Geosciences (doctoral course)
    2018-2019-2020, France, more details here