Postdoctoral position : Machine learning based eddy closures for ocean models – CNRS at Institute of Geosciences and Environment, Grenoble, France

Our research group is advertizing for a postdoctoral researcher position to work for CNRS at Institute of Geosciences and Environment, Grenoble, France.


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.

Working context

The selected candidate will work at the Institute for Environmental Geosciences (IGE), in Grenoble, in the French Alps. This is a public research institute under the affiliation of CNRS, IRD, Univ. Grenoble Alpes, Grenoble-INP and INRAE. It brings together about 250 people, including 150 permanent members (researchers, teacher-researchers, engineers) and about a hundred contractual agents (doctoral students, postdocs, engineers and technicians). The institute also welcomes several dozen trainees and scientific visitors every year. It is spread over three sites of the Grenoble University Campus that are 5 minutes away from each other. IGE is one of the main institutes within the Observatoire des Sciences de l’Univers de Grenoble (OSUG) which is a federative structure of INSU.

The selected candidate will join the MEOM group, which focuses on ocean/sea-ice modeling and prediction (see, and will be supervised jointly by Julien Le Sommer (IGE) and Julie Deshayes (LOCEAN, Paris). This work will involve strong interactions with the participants of the M2LINES ( Key collaborators will include Laure Zanna (NYU) and Alistair Adcroft (GFDL).

Scientific context

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 model to another.


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 (

Requirements and selection criteria

The selected candidate will hold a PhD in physical oceanography or in computational fluid dynamics. The selection will be based on the following scientific and technical criteria:

  • Demonstrated experience in ocean modeling (using NEMO or other models)
  • Good understanding of oceanic eddy processes and their parameterization
  • Demonstrated experience Fortran and Python coding
  • Demonstrated experience in scientific writing
  • Experience with one the prominent machine learning libraries (PyTorch, TensorFlow) (not compulsory);
  • Motivation to disseminate scientific results;
  • Demonstrated 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.

Job information

Employer: CNRS.

Type of contract: fixed-term.

CNRS Section: 19.

Duration of contract: 12 months (renewable).

Expected date of hire: April to July 2023.

Work quota: Full time.

Required level of studies: PhD.

Required experience: No postdoctoral experience required.

Gross salary: from 2800€/month to 4100€/month (depending on experience).

Paid leave: approximately 45 days per year.

Health care: France runs a statutory health insurance system providing universal coverage for its residents (Sécurité Sociale). Most residents additionally pay for a complementary private health insurance for expenses not covered by the statutory health insurance (Mutuelle).

If you are interested:

Contact us: and

Apply on the CNRS website before April 15th

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