Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : Institut des Geosciences de l’Environnement, Gren
Durée : 18 months (renewable
Contact : Julien.Lesommer@univ-grenoble-alpes.fr
Date limite de publication : 2023-04-25
The combination of machine learning with scientific computing is an active area of research which is expected to improve geoscientific models and their integration into broader numerical systems, such as climate models and operational forecasting systems. A key practical question for these models is to define how machine learning components can be encoded and maintained into pre-existing legacy codes, written in low level abstraction languages (as FORTRAN). Several practical options exist, each coming with pros and cons. Neural networks may for instance directly be implemented in FORTRAN, one could alternatively use the C/C++-bindings of specific machine learning libraries or more generic high level coupling interfaces. But the trade-offs between these different strategies are usually model and use-case specific.
The NEMO ocean / sea-ice model (https://www.nemo-ocean.eu) and the CROCO ocean model (https://www.croco-ocean.org) are two important tools for the oceanographic community, in particular in the context of operational forecasting systems and european Earth System Models. Their development roadmaps involve the definition of sustainable interfaces for trainable components to be leveraged on-line during model simulations. A working group dedicated to machine learning related developments has been set-up as part of the NEMO development team.
The selected candidate will be in charge of providing quantitative information and developing practical solutions for a sustainable implementation of trainable components into the NEMO and the CROCO ocean models. This will involve defining benchmark use-cases of machine learning based components in ocean models. These will be based for instance on subgrid parameterizations already developed as part of the M2LINES project. The selected candidate will define quantitative metrics for intercomparing the different available options for coupling AI-based trainable components and legacy ocean models, and implement several options into the NEMO and CROCO ocean models. Possible options may include Infero (https://github.com/ecmwf-projects/infero), ICCS Fortran ML Bridge (https://github.com/Cambridge-ICCS/fortran-ml-bridge), HPE SmartSim (https://github.com/CrayLabs/SmartSim), Melissa (https://gitlab.inria.fr/melissa) or OASIS (https://oasis.cerfacs.fr/en/). The work will then involve performing systematic intercomparison based on realistic model simulations to be performed on HPC resources. The selected candidate will then write reports on the results and present the outcome of the work to relevant working group and project meetings. He/She will participate also in the discussions and meetings of the M2LINES and MEDIATION projects.
Profil du candidat :
The selected candidate will hold a MSc in computer science, engineer or PhD.
Formation et compétences requises :
The selection will be based on the following scientific and technical criteria: demonstrated experience in High Performance Computing; demonstrated experience in Fortran/C/C++ and Python coding; demonstrated experience in (at least) one of the prominent machine learning frameworks (PyTorch, TensorFlow,… ); basic understanding of Computational Fluid Dynamics and subgrid closures for fluid flows;
experience in running atmospheric, ocean circulation or climate models (not compulsory); demonstrated ability to work within a team.
The selection panel will also consider the gender balance of the entire research team. Junior candidates with a fresh title are also welcome.
This position may help you build a curriculum in the very active domain of hybridization between Numerical Simulation and Deep Learning (ML4Sci)
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/research-engineer-ml/
Please contact : firstname.lastname@example.org and email@example.com
Review of applications will begin immediately and continue until the position is filled.