Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : Institut des Geosciences de l’Environnement, Gren
Durée : 12 months (renewable
Contact : Julien.Lesommer@univ-grenoble-alpes.fr
Date limite de publication : 2023-04-25
Mesoscale eddies are essential oceanic processes and their effect needs to be accurately represented in ocean components of climate models. In these models, the representation of mesoscale eddy processes affects the simulated means states, but also the overall variability and the response to changing conditions. Yet, because the spatial scales of mesoscale eddies are not explicitly represented in most ocean components of climate models, their effect is accounted for by subgrid closures.
The design of eddy closures for ocean models is an active field of research. With the development of scientific machine learning and its applications to fluid simulations, several eddy closures based on deep learning have been proposed (see Zanna and Bolton 2021). However, to date there has been no systematic evaluation of the impact of these new closures in full-scale realistic simulations. An important question is in particular whether their performance can be easily transferred from one ocean model to another.
The general mission is to conduct research work investigating the impact of machine learning based mesoscale eddy closures in ocean circulation models. The selected candidate will contribute to the M2LINES international project.
The selected candidate will contribute to a joint study aiming at analyzing the impact of several machine learning based eddy closures across different ocean models as part of the M2LINES international project. The work will specifically focus on the scheme proposed by Guillaumin and Zanna (2021) and its impact in the NEMO and MOM6 ocean circulation models. The selected candidate will be in charge of defining a test bed (simulation protocols, evaluation metrics) for assessing the impact of eddy closures in the NEMO 1/4° global ocean model (eORCA025). The work will then focus on refining the implementation of the Guillaumin and Zanna (2021) scheme in the NEMO ocean model and on performing a series of (ocean-only) model experiments. He/she will then analyze the results and contribute to the comparison with a companion effort with the MOM6 ocean model.
The work will be developed and implemented in close coordination with the MOM6 team, as part of the M2LINES collaboration. An important part of the work is therefore the participation in the M2LINES project activities (group meetings, seminars, etc). Regular visits to LOCEAN in Paris will also be required. The selected candidate will be expected to monitor upcoming publications, to write scientific articles, to present results in international conferences and to the relevant NEMO working groups (https://forge.nemo-ocean.eu/wgs).
Profil du candidat :
The selected candidate will hold a PhD in physical oceanography or in computational fluid dynamics, or computer science.
Formation et compétences requises :
The selection will be based on the following scientific and technical criteria: experience in geoscientific modeling, understanding of oceanic processes, experience Fortran and Python coding, experience in scientific writing, experience with one the prominent machine learning libraries (PyTorch, TensorFlow) (not compulsory); motivation to disseminate scientific results; ability to work within a team and in an international context.
The selection panel will also consider the gender balance of the entire research team.
Adresse d’emploi :
Institut des Géosciences de l’Environnement, Maison Climat Planète, 70 rue de la Physique, Domaine Universitaire, 38400 St Martin d’Hères
More information : https://lesommer.github.io/2023/02/15/postdoc-eddy-params-ml/
Please contact : firstname.lastname@example.org and email@example.com
Review of applications will begin immediately and continue until the position is filled.