Postdoctoral Appointee - Computational Chemistry

Postdoctoral Appointee - Computational Chemistry

Argonne National Laboratory is seeking a Postdoctoral Appointee who will conduct cutting‑edge research in AI for Materials Science Discovery, focused on critical materials complexation and conversion, and energy storage and conversion within the Data Science and Learning Division (DSL) of the Computing, Environment and Life Sciences Directorate (CELS) and the Materials Science Division (MSD) of the Physical Sciences and Engineering Directorate (PSE).

The project involves:

• Materials Development Platform that will enable redox molecule optimization via predictive simulations, database management, and AI/ML model development to design and discover redox‑active materials with tunable properties (structure, charge state, etc.).
• Discovery of novel materials for energy storage and conversion and their characterization with advanced computational chemistry tools, including molecular dynamics, density functional theory and Grand Canonical Monte Carlo simulations.

• A recent or soon‑to‑be‑completed PhD (within the last 0‑5 years) in Materials Science, Computational Materials Science, Chemical Engineering or a closely related field.
• Comprehensive understanding of applied computational materials science, including electronic structure methods, molecular dynamics, and scientific machine learning.
• Experience with High‑Performance Computing (HPC) systems and intelligent workflows.
• Demonstrated expertise combining AI and computational chemistry software in HPC platforms for materials science discovery research.
• Effective written and oral communication skills.
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
• Expertise using ALCF/OLCF HPC platforms will be a plus.