Postdoctoral researcher in computational oceanography
The postdoctoral researcher will use a coarse graining approach applied to high resolution ocean numerical simulations and Deep Neural Networks in order to formulate parameterizations of the impact of unresolved turbulent processes in coarser resolution ocean models. The work will involve analysing several kilometric resolution ocean model simulations through a cloud-based system and formulating inverse problems for estimating the contribution of unresolved processes on ocean dynamics from coarse grained information. The inverse problems will then be solved with physics-aware machine learning algorithms based on Deep Neural Networks. The learned subgrid closures will eventually be tested within the NEMO-OCE ocean model (https://www.nemo-ocean.eu) using SmartSim (https://github.com/CrayLabs/SmartSim) in order to assess their a posteriori skills in realistic simulations. The subgrid closures will focus in priority on the representation of the impact of submesoscale variability on air-sea interactions and ocean surface boundary layer dynamics in ocean climate models. The research will be conducted as part of the M2LINES project (https://m2lines.github.io) and will involve several international collaborators.
Skills: The expected candidates should hold a PhD in physical oceanography, atmospheric science or computational fluid dynamics. Their research track record should demonstrate their ability to carry out cutting edge research in one of the following fields : ocean fine scale processes, ocean modelling, turbulent closures, data-driven large eddy simulation via machine learning. Their research background should demonstrate their strong interest in approaches bridging physical science and computational science. Computational skills should include Python, FORTRAN and some experience with one of the prominent software libraries in machine learning (in particular PyTorch or TensorFlow). They should speak and write English fluently and be able to interact in a multicultural environment. They will need to demonstrate curiosity, autonomy and initiative.
Work context: The recruited researcher will work at the Institute of Environmental Geosciences (IGE) within the MEOM group in the framework of the M2LINES project. The IGE (http://www.ige-grenoble.fr) is a joint research unit bringing together staff from the UGA, CNRS, Grenoble INP and IRD who are interested in the study of the climate and the anthropisation of our planet. The MEOM group (http://meom-group.github.io) brings together 25 researchers, engineers, students and postdocs in the field computational oceanography. The M2LINES project (https://m2lines.github.io) is a large international collaborative project with the goal of improving climate projections using scientific Machine Learning. Activities will involve regular team meetings with the international consortium.
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