Deep Learning for Modeling Ocean–Ice Shelf Interactions (M/F)

Updated: 8 days ago
Location: Saint Martin, MIDI PYRENEES
Job Type: FullTime
Deadline: 01 May 2026

11 Apr 2026
Job Information
Organisation/Company

CNRS
Department

Institut des géosciences de l'environnement
Research Field

Environmental science
Environmental science » Earth science
Environmental science » Global change
Researcher Profile

First Stage Researcher (R1)
Application Deadline

1 May 2026 - 23:59 (UTC)
Country

France
Type of Contract

Temporary
Job Status

Full-time
Hours Per Week

35
Offer Starting Date

1 Aug 2026
Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The overall objective is to improve the integration of polar ice sheets into Earth system models by using neural network emulators at the interface between an Antarctic ice sheet model (Elmer/Ice) and a global ocean model (NEMO). The selected candidate will contribute to the ANR-AIAI project (https://anr-aiai.github.io ).

Scientific Context
The melting of Antarctic ice shelves by the ocean is a major source of uncertainty regarding global climate change and sea level rise. Accurately representing ice-ocean interactions beneath ice shelves is a major challenge due to the coarse spatial resolution of climate models. Simple parameterizations exist to represent melting beneath unresolved or partially resolved ice shelves, but they generally fail to capture the complexity of the response to changing ocean conditions (Burgard et al., 2022). The application of deep learning to this problem has yielded promising results (Rosier et al., 2023; Burgard et al., 2023). Further development and refinement of these techniques should therefore significantly improve the representation of basal melting in simulations.

• Develop a neural network interface to simulate subglacial melting in the NEMO ocean model.
• Present the results at international conferences.
• Participate in the AIAI project and IGE activities (meetings, seminars, etc.).
• Keep up to date with publications on the subject and write scientific articles

The successful candidate will be assigned to the Institute of Environmental Geosciences (IGE). This is a public research laboratory under the supervision of the CNRS, the IRD, the University of Grenoble Alpes, Grenoble-INP, and INRAE, which focuses on climate change and human impact on our planet in polar, mountain, and intertropical regions—areas that are particularly sensitive and face major societal challenges. It brings together approximately 300 people, including more than 180 permanent staff members (researchers, faculty-researchers, engineers, and technicians) and approximately 120 staff on fixed-term contracts (doctoral students, researchers, and IT staff). The laboratory also hosts several dozen interns and visiting scientists each year. The laboratory is located at three sites on the Grenoble university campus, all within a few minutes' walk of each other. The IGE is one of the main laboratories of the Grenoble Observatory of Universe Sciences (OSUG), a federative structure of the INSU. The successful candidate will join the CryoDyn team, which focuses on ice dynamics and their connections to the climate system. This person will be supervised by Nicolas Jourdain (IGE) and Clara Burgard (IPSL-LOCEAN, Paris). There will be close collaboration with IPSL-LSCE (Cécile Agosta).


Where to apply
Website
https://emploi.cnrs.fr/Offres/CDD/UMR5001-SANASK0-002/Default.aspx

Requirements
Research Field
Environmental science
Education Level
PhD or equivalent

Research Field
Environmental science
Education Level
PhD or equivalent

Research Field
Environmental science
Education Level
PhD or equivalent

Languages
FRENCH
Level
Basic

Research Field
Environmental science
Years of Research Experience
None

Research Field
Environmental science » Earth science
Years of Research Experience
None

Research Field
Environmental science » Global change
Years of Research Experience
None

Additional Information
Eligibility criteria

Selection will be based on the following scientific and technical criteria:
• Proven experience with deep learning methods.
• Proven experience with Python programming.
• Proven experience in writing scientific papers.
• General knowledge of physical oceanography or climate dynamics.
The selection committee will take gender balance within the research team into account.


Website for additional job details

https://emploi.cnrs.fr/Offres/CDD/UMR5001-SANASK0-002/Default.aspx

Work Location(s)
Number of offers available
1
Company/Institute
Institut des géosciences de l'environnement
Country
France
City
ST MARTIN D HERES
Geofield


Contact
City

ST MARTIN D HERES

STATUS: EXPIRED

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