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Extreme Value Analysis of ocean dynamics by coupling machine learning and extreme value theory

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Français - Anglais

Date Limite Candidature : vendredi 6 novembre 2020

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General information

Reference : UMR8212-PHINAV-001
Workplace : GIF SUR YVETTE,GIF SUR YVETTE
Date of publication : Friday, October 16, 2020
Scientific Responsible name : Philippe Naveau
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 4 January 2021
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Extremes are crucial features of geophysical processes and can play a
fundamental role in terms of societal impacts, e.g. major floods. By definition,
extreme events are rare, but they happen and records are made to be beaten. In
terms of machine learning algorithms, it is difficult to learn from very few
examples even in a large learning database. In addition, the probability
distribution of extreme events cannot be well captured by measures based solely
on deviations from the mean. These two issues clearly challenge the classic
learning paradigm. From an uncertainty point of view, there exists a probability
theory tailored to model extremal behavior, the so-called Extreme Value Theory
(EVT).
The main task of the PhD student will be to build bridges between neural
networks (NN) used in ocean dynamics, and multivariate EVT used in environmental
statistics.
A major bottleneck to couple both NN and EVT techniques is the question of
metrics for rare events, and how to assess predictive distributions from
forecast models. This two aspects will be studied in detail during the PhD.
This PhD will be part of the ANR Melody. This implies that the main application
domain will be the field of ocean dynamics and consequently, all algorithms will
be tested on low--dimensional toy models (Lorenz models) or intermediate size
models (1D-Burgers equations, 2D-QG models, etc).
PROFIL OF THE MASTER STUDENT
Data science, statistical learning and/or geosciences (ocean dynamics)

Work Context

Extremes are crucial features of geophysical processes and can play a
fundamental role in terms of societal impacts, e.g. major floods. By definition,
extreme events are rare, but they happen and records are made to be beaten. In
terms of machine learning algorithms, it is difficult to learn from very few
examples even in a large learning database. In addition, the probability
distribution of extreme events cannot be well captured by measures based solely
on deviations from the mean. These two issues clearly challenge the classic
learning paradigm. From an uncertainty point of view, there exists a probability
theory tailored to model extremal behavior, the so-called Extreme Value Theory
(EVT).
The main task of the PhD student will be to build bridges between neural
networks (NN) used in ocean dynamics, and multivariate EVT used in environmental
statistics.
A major bottleneck to couple both NN and EVT techniques is the question of
metrics for rare events, and how to assess predictive distributions from
forecast models. This two aspects will be studied in detail during the PhD.
This PhD will be part of the ANR Melody. This implies that the main application
domain will be the field of ocean dynamics and consequently, all algorithms will
be tested on low--dimensional toy models (Lorenz models) or intermediate size
models (1D-Burgers equations, 2D-QG models, etc).
PROFIL OF THE MASTER STUDENT
Data science, statistical learning and/or geosciences (ocean dynamics)

Constraints and risks

None

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