Postdoctoral Scholar – AI-Driven Autonomous Materials Discovery at Berkeley Lab, USA

The Energy Technologies and Systems Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is inviting applications for a Postdoctoral Scholar position. The selected candidate will join the prestigious Jain (HackingMaterials) group within the Applied Energy Materials group, working at the leading edge of autonomous science, generative AI, and high-throughput computational materials discovery.

Position Overview

Research Projects

This postdoctoral fellowship bridges two major pioneering autonomous science initiatives:

• A-WILD (First 9 Months): Part of the DOE Genesis Mission, focusing on building a self-improving, closed-loop discovery engine. You will couple AI hypothesis generation with the A-Lab (Berkeley Lab’s fully autonomous inorganic synthesis laboratory) and develop simulation agents to predict properties such as Li-ion conductivity in solid electrolytes at scale.
• PANDORA: An ARPA-E-funded closed-loop AI platform targeting catalyst discovery for CO₂ conversion into fuels and chemicals. You will oversee first-principles active-site descriptor modeling, high-throughput screening using machine-learned potentials, and the development of a dedicated catalysis database.

Job Description & Responsibilities

The candidate will play a central role in proving quantitatively that AI-driven autonomous workflows accelerate scientific discovery faster than conventional human iteration. Key duties include:

• Developing autonomous simulation agents to evaluate Li-ion conductivity and related transport properties with full uncertainty quantification and provenance tracking.
• Scaling MLIP, molecular dynamics (MD), and density functional theory (DFT) workflows (using atomate2, MACE, CHGNet, or UMA-class potentials) across High-Performance Computing (HPC) environments.
• Validating computational predictions against physical synthesis and characterization data from A-Lab via AlabOS APIs.
• Calculating first-principles active-site descriptors for CO₂ reduction catalysts (e.g., vacancy formation energies, adsorption energies, metal-support interactions) and correlating them with experimental kinetics.
• Deploying data, agentic frameworks, and benchmark datasets via the Materials Project and Genesis American Science Cloud (AmSC).
• Publishing high-impact research findings in peer-reviewed scientific journals and presenting at major conferences.

Eligibility & Qualifications

Required Qualifications:

• Education: Ph.D. in Materials Science, Chemistry, Physics, Computer Science, or a closely related discipline.
• Experience Limit: Must have less than 3 years of paid postdoctoral research experience.
• Programming Skills: Demonstrable, advanced Python programming capabilities (a programming portfolio such as GitHub is required).
• Computational Expertise: Hands-on experience with Machine-Learned Interatomic Potentials (MLIPs) and first-principles DFT workflows (e.g., VASP, atomate2).
• HPC Proficiency: Proven experience launching and managing high-throughput computational workloads on supercomputing architectures.
• Domain Knowledge: Strong theoretical and practical foundation in solid-state materials science.
• Teamwork: Capacity to drive research independently while collaborating seamlessly in a multi-institutional team environment.

Desired Qualifications:

• Prior experience developing or applying agentic workflows and multi-agent AI architectures.

Benefits & Working at Berkeley Lab

Berkeley Lab postdocs receive competitive benefits through the University of California system, including:

• Comprehensive medical, dental, and vision insurance coverage.
• Retirement savings through the UC Defined Contribution Safe Harbor Plan with voluntary options.
• 24 days of paid Personal Time Off (PTO), plus sick leave and official laboratory holidays.
• Up to 8 weeks of Postdoctoral Paid Family Leave.
• Professional development, networking, and scientific community engagement via the Berkeley Lab Postdoc Association.

How to Apply

Interested candidates should apply directly through the Berkeley Lab careers portal. Please prepare and upload the following documents:

1. Cover Letter: Detailing your research background, alignment with autonomous materials design, and specific interest in the position.
2. Curriculum Vitae (CV) / Resume: Including publication record and links to code/programming portfolios (e.g., GitHub, GitLab).

Last Date for Apply

Open Until Filled: Applications will be accepted and reviewed on an ongoing basis until the posting is officially removed.

Apply Online at Berkeley Lab Careers