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Portail > Offres > Offre UMR5672-FREBOU-003 - Post-doc position H/F - Calcul des événements climatiques extrêmes avec des algorithmes d'événements rares et des approches de machine learning

Post-doc position H/F - Computing climate extreme events using machine learning and rare events algorithms

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : vendredi 6 août 2021

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General information

Reference : UMR5672-FREBOU-003
Workplace : LYON 07
Date of publication : Friday, July 16, 2021
Type of Contract : FTC Scientist
Contract Period : 24 months
Expected date of employment : 15 September 2021
Proportion of work : Full time
Remuneration : Selon les grilles du CNRS. A titre indicatif 2675€ brut mensuel. Le montant exact est calculé par l'administration du CNRS
Desired level of education : PhD
Experience required : 1 to 4 years

Missions

We have recently demonstrated that rare event algorithms can lead to a gain of a factor 100 to 1000 in the computational cost required to compute extreme events in climate models, for instance extreme heat waves over Europe [1]. This technique will probably have a huge impact in the future for the study of climate extremes. We demonstrated that this technique is effective for persistent extremes and can be used with some of the best climate models used for CMIP IPPC experiments.
Making similar advances for other classes of extremes, with a more complex dynamics, requires new theoretical and methodological developments. We need to learn effective dynamics of the large scales of the turbulent flow related to extreme simulations, and from these effective dynamics learn optimal score functions for the rare event algorithms, called committor functions [2].
The aim of this post-doc will be to develop and implement the methodology to learn committor functions from already produced climate model outputs, using machine learning and stochastic weather generators [3]. The machine learning approach will be developed in an interdisciplinary team that gathers specialists of computer science, machine learning, climate dynamics, data sciences and statistical physics.

Activities

Organize his own research on the subject and participate to the group management.

Skills

We are seeking excellent candidates, holding a PhD in machine learning, computer science, climate dynamics, physics or in mathematics is required.

Work Context

This position is part of the project SAMPRACE, funded by ANR, a collaboration between LSCE, IPSL, Saclay, Paris (Pascal Yiou and Davide Faranda) and ENS de Lyon and CNRS (Freddy Bouchet and Corentin Herbert). The research project gathers groups in physics and statistical physics (LPENSL), computer sciences (LIP/ENSL), and statistics and climate sciences (LSCE, IPSL). The post-doc will participate to the project interdisciplinary discussions and meetings.

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