After a BSc and a MSc in Physics (University of Lorraine/CentraleSupélec), Sébastien started its PhD in October 2019 as part of a collaboration between the SIMaP (Materials science laboratory), the LIG (Informatics laboratory) and the Fourier Institute IF (Mathematics). The purpose of its PhD is to use Machine Learning approaches to analyze large amounts of data generated by atomistic modelling, in order to provide a better understanding of the crystallization mechanisms of materials and a more general view of the structure at the atomic scale.
Thesis subject
Machine Learning Approaches in Materials Science: structural and thermodynamic identification at the atomic scale. Under the direction of Noël Jakse (SIMaP), Emilie Devijver (LIG) & Rémi Molinier (IF).
