Informations générales
Intitulé de l'offre : M/W PhD thesis Statistics and machine learning for predicting complex outputs with applications to nuclear safety (H/F)
Référence : UMR5219-FRABAC-001
Nombre de Postes : 1
Lieu de travail : TOULOUSE
Date de publication : lundi 22 mai 2023
Type de contrat : CDD Doctorant/Contrat doctoral
Durée du contrat : 36 mois
Date de début de la thèse : 15 novembre 2023
Quotité de travail : Temps complet
Rémunération : 2 135,00 € gross monthly
Section(s) CN : Mathematics and mathematical interactions
Description du sujet de thèse
The prediction of scalar-valued or vector-valued outputs is now well understood in statistics and machine learning. However, in many applications, one would be rather interested in predicting a quantity of interest taking values in more complex spaces, such as geometric manifolds, spaces of measures, and Euclidean subspaces with non-linear constraints.
Several possibilities will be explored, focusing in particular on predicting measures. The developed methodology will be applied to real problems in computational fluid dynamics, in collaboration with the Radioprotection and Nuclear Safety Institute (IRSN). Examples of outputs are then flow images and histograms of physical quantities of interest. The industrial motivation is then a better understanding of two-phase flow in steam generators of nuclear plants.
Contexte de travail
Institut de mathématiques de Toulouse. The environment is rich, with many scientific events.