Postdoctoral Appointee - NST - Machine Learning for Electronic Structure and Materials Modeling

The postdoctoral appointee will engage in research at the intersection of machine learning and electronic structure calculations, focusing on developing algorithms and software to enhance the computation of electronic structure properties and advanced sampling techniques. This role is part of a collaborative effort within the Midwest Integrated Center for Computational Materials at Argonne National Laboratory and the University of Chicago, aimed at advancing computational materials science.