PhD and postdoctoral opportunities are immediately available in the mechanical and industrial engineering department at the University of Illinois, Chicago and Argonne National Lab in the broad area of AI and machine learning (ML) guided materials discovery and design. The students will work on applying ML and AI for developing first-principles based atomistic simulation models, inverse design of materials and for integrating atomistic simulations with X-ray/microscopy for better interpretation of experimental data. Application areas include energy storage, tribology, catalysis and neuromorphic computing.

We are specifically looking for motivated graduate students/early-stage researchers, with interest in AI and machine learning guided modeling of materials. Expertise in molecular dynamics simulations and/or density-functional theory (DFT) for reactive simulations will be considered a plus. The students will work in a dynamic team of UIC faculty as well as staff scientists at Argonne National Laboratory. Within the team we have extensive experience with molecular dynamics simulations, reactive empirical force fields, chemical dynamics, numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and algorithm development in conjunction with extensive applications in the fields of nanoscience and energy-related materials (https://www.anl.gov/profile/subramanian-sankaranarayanan).

The project will also involve significant interactions between the staff scientists at various divisions (NST, APS, MCS) within Argonne National Lab. Interested candidates should contact Dr. Subramanian Sankaranarayanan (skrssank@uic.edu and skrssank@anl.gov)