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Development of machine learning methods to combine multi-model biases in studies of detection and attribution of climatic extremes

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Français - Anglais

Date Limite Candidature : mercredi 29 septembre 2021

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General information

Reference : UMR8212-PHINAV-003
Workplace : GIF SUR YVETTE
Date of publication : Wednesday, September 8, 2021
Scientific Responsible name : Naveau Philippe
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 November 2021
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Global climate models are generally assessed on average behaviours and typical fluctuations, and biases are thus determined. The aim of this PhD will be to set up a new framework for the evaluation of model biases for extreme events. The goal is to develop a theoretical framework to combine the relative merits of different models according to their ability to reproduce specific extremes: heatwaves and extreme precipitation. Extreme value theory and statistical physics will be at the heart of the analysis and will be coupled with machine learning techniques dedicated to the aggregation of biases and uncertainties. Once the conceptual framework, the data from the CMIP experiments will be analysed in detail. The algorithms developed may be useful for other types of risk analysis.

Work Context

The «Extrêmes-Statistiques-Impacts-Régionalisation» LSCE team (Philippe Naveau, Davide Faranda), near Paris, and the Climate and statistical mechanics” group (ENS, Lyon, Freddy Bouchet) will be strongly involved in the PhD supervision.
Collaborations with P. Ailliot (Math dept, UBO, Brest) and P. Tandeo (IMT Atlantique, Brest) will be also established.

Additional Information

The PhD is funded by a CNRS 80 PRIME grant for a three year period.

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