PhD studentships on atomistic modeling of battery materials
| Published | May 7, 2023 |
| Location | Grenoble, France |
| Category | Machine Learning |
| Job Type | Scholarship |
Description
Offer Description
Two PhD studentships are available at CEA-Grenoble, to do computational modeling of new battery materials using ab initio and machine learning techniques.
Topic 1: Ab initio and machine learning modeling of the Solid-Electrolyte Interphase (SEI) growth mechanisms in solid state batteries
Despite being crucial for battery performance, degradation, the SEI’s growth mechanisms are still a matter of controversy. The thesis will develop new atomistic techniques to predict the initial stage growth of the SEI in sulfide based battery materials and elucidate the actual mechanisms at play.
Topic 2: From atomic structure to thick electrode in sodium-ion batteries
Sodium-ion batteries are particularly promising for stationary storage applications. In this thesis, we propose to explore the properties of active materials for the positive electrode of sodium-ion batteries. Beyond improving our understanding of the materials, which is essential for this emergent technology, this thesis will couple theory and experiment to develop a multiscale approach, from the atomic structure to the thick electrode.
Funding category: Contrat doctoral
CTBU, CFR
PHD title: Doctorat de Physique
PHD Country: France
Requirements
We are looking for highly motivated candidates who are proficient in computer programming and mathematical modeling. Candidates should hold the equivalent of a master degree in physics, computational chemistry, or materials science.
