-
closely with fellow group members, as well as with research teams from the broader Argonne community, the University of Chicago Pritzker School of Molecular Engineering, and our Q-NEXT partners. In
-
successful candidate will conduct research focused on meso- and macroscale mathematical modeling of next-generation batteries, while leveraging advanced artificial intelligence (AI) tools to accelerate
-
integrating literature, in-house, and newly generated experimental data Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and
-
bonding, defects, catalysis, batteries, solid-state chemistry, molecular systems, or related materials classes. Strong Python skills and familiarity with LLM APIs, agent frameworks , PyTorch, and the Python
-
modeling and techno-economic assessment as appropriate. The candidate will work independently under general guidance, collaborate effectively within a multidisciplinary team environment, and prepare