Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : Mines Saint-Etienne, BRGM and IRSN
Durée : 36 months
Contact : phd.math4flood@listes.emse.fr
Date limite de publication : 2020-06-30

Contexte :
The risk of coastal flooding is aggravated by the failure of coastal defenses (either natural like dunes or artificial like dykes). In numerical simulations, such processes are typically accounted for by defining a set of scenarii describing for instance the possible spatial location, time duration, erosion height / width of the failures. In the current PhD, we propose to develop a systematic mathematical procedure to characterize the possible combinations of conditions (named excursion set) that lead to flooding.
The PhD candidate will participate to the continuation of the OQUAIDO collaborative project (http://chaire-mathematiques-appliquees.emse.fr/) and benefit from numerous interactions with other researchers in the same scientific domain.

Sujet :
The PhD work involves the inversion of the numerical models that simulate the floods. In order to alleviate the computational cost of this task, we build upon the combination of metamodelling techniques (Gaussian processes) and active learning specifically dedicated to the estimation of excursion set. The PhD aims at improving the existing methods in two ways:
(1) methodologically, by making the inversion robust to extreme-but-rare events and accounting for uncertainties in the numerical models;
(2) operationally, by assessing how this approach can help in the communication and the management of the risk through better high
dimensional visualization of the excursion set and the decomposition of the uncertainties.
The application cases will focus on marine (BRGM) and river (IRSN) flooding.

Profil du candidat :
The candidate should have strong interest in applied mathematics, probability/statistics, data science, numerical methods.
The candidate may also be motivated by applications in the field of environmental risk.
The candidate should like working in a team.

Formation et compétences requises :
– Hold a master’s degree in applied mathematics: probability/statistics, machine learning, data science, optimization,…
– Have a strong background in scientific programming using (Python, Matlab/Octave, R for example)
– Have English skills allowing scientific communication (oral/reading/writing)

Adresse d’emploi :
Saint-Etienne (Fr) and/or Orléans and/or Paris (negociable)

Document attaché : these_BRGM_IRSN_LIMOS_Eng.pdf