Postdoctoral Scholar - AI-Driven Materials Discovery at Lawrence Berkeley National Laboratory

Postdoctoral Scholar - AI-Driven Materials Discovery

Lawrence Berkeley National Lab’s (LBNL) Energy Storage & Distributed Resources Division has an opening for a Postdoctoral Scholar in AI-Driven Materials Discovery to join the team. In this exciting role, you will play a pivotal role in an ambitious, multi-institutional, DOE‑funded effort to build the next generation of AI for scientific discovery. This position offers a unique opportunity to work at the frontier of computational materials science, combining machine‑learned interatomic potentials (MLIPs), density functional theory (DFT), and large‑scale machine learning on some of the world’s fastest supercomputers.

The scholar will develop and train advanced ML models, design scalable autonomous simulation workflows, and contribute to multimodal AI systems that integrate physics, data, and reasoning. Working closely with collaborators across Berkeley Lab and partner institutions, the scholar will have access to world‑class HPC resources and the one‑of‑a‑kind A‑Lab automated synthesis facility, enabling research that bridges simulation and experiment. This position is ideal for a creative and highly skilled computational scientist eager to push the boundaries of AI‑driven materials discovery and establish themselves as a leader in the field.

What You Will Do:

• Develop, train, and validate machine‑learned interatomic potentials for a wide range of inorganic materials.
• Build and optimize high‑throughput DFT workflows for property prediction and benchmarking against MLIPs.
• Train and scale machine learning models on leadership‑class HPC resources (NERSC, ALCF, etc.) using parallel and distributed computing approaches.
• Contribute to the development of simulation agents that autonomously couple MLIPs, DFT, and molecular dynamics for multiscale modeling.
• Integrate computational results into multimodal foundation models and agentic AI frameworks developed within the overall project.
• Collaborate with computational and experimental scientists to address benchmark materials science problems.
• Publish results in peer‑reviewed journals, present at conferences, and contribute to open‑source software tools.

Additional Responsibilities as needed:

• Assist in outreach activities, workshops, and tutorials to disseminate project outcomes.

What is

Required:

• Ph.D. in Materials Science, Physics, Chemistry, Computer Science, or a related field.
• Demonstrated expertise in training machine learning models, preferably for atomistic or materials science applications.
• Experience with MLIP frameworks (NequIP, MACE, CHGNet, GAP, SNAP) and integration with DFT.
• Strong background in DFT methods and workflows (VASP, Quantum ESPRESSO, WIEN2k).
• Proficiency in Python and experience with large‑scale parallelization and HPC environments.
• Strong publication record in computational materials science, AI/ML for science, or related fields.
• Ability to work independently as well as collaboratively within a large, multi‑institutional team. Note that this position will be on‑site.

Desired

Qualifications:

• Experience with agentic AI or autonomous workflows for science.
• Familiarity with experimental data or integration of simulations with experimental workflows.
• Knowledge of molecular dynamics simulations and force field development (LAMMPS, ASE).
• Prior contributions to open‑source scientific software.

For consideration, please apply by October 1, 2025 with the following application materials:

• Cover Letter - Describe your interest in this position and the relevance of your background.
• Curriculum Vitae (CV) or Resume.

Notes:

• This is a full‑time, 2 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post‑degree.
• This position is represented by a union for collective bargaining purposes.
• The monthly salary range for this position is $7,790 / mo - $8,701.00 / mo and is expected to start at $7,790 / mo or above. Postdoctoral positions are paid on a step schedule per union contract and…