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.

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, materials science, or a related discipline.

The team: The computational materials physics group at the battery-modeling laboratory of CEA-Grenoble is part of the Grenoble-Alpes University. The location enjoys a rich scientific and technological environment, with excellent networking opportunities, and good living standards (https://www.nature.com/articles/d41586-023-00109-x). The standard PhD completion time in France is three years.

To apply: please send a detailed CV, application letter, and the names and email addresses of three references, as soon as possible, to [email protected] and [email protected]. The positions are to start anytime between March-October 2023.