AI-Driven Materials Discovery Postdoc

Lawrence Berkeley National Laboratory; Berkeley (CA); Presencial; USD 92.000 - 103.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health benefits

Retirement plan

PTO 24 days per year

Postdoc Paid Family Leave

Postdoc Association

Descripción de la vacante

Lawrence Berkeley National Laboratory's Energy Technologies and Systems Division seeks a Postdoctoral Scholar to join the Jain (HackingMaterials) group for autonomous science projects, building simulation agents to predict materials properties and enabling live experimental campaigns at Berkeley Lab.

The role starts November 1, 2026, is a full-time, two-year appointment with potential renewal, and involves collaboration across multi-institution teams to advance AI-driven discovery in catalysis

Formación

• Ph.D. in a relevant field (Materials Science, Chemistry, Physics, CS) is required.
• Demonstrated strong Python programming skills.

Responsabilidades

• Develop autonomous simulation agents to predict Li-ion conductivity and related properties.
• Build and scale MLIP/MD/DFT workflows on HPC infrastructure.
• Validate predictions against experimental campaigns and integrate into live campaigns.

Conocimientos

Python programming

HPC experience

Materials science knowledge

Educación

Ph.D. in Materials Science, Chemistry, Physics, Computer Science, or related field

Herramientas

VASP

atomate2

Descripción del empleo

Lawrence Berkeley National Laboratory's Energy Technologies and Systems Division seeks a Postdoctoral Scholar to join the Jain (HackingMaterials) group for autonomous science projects, building simulation agents to predict materials properties and enabling live experimental campaigns at Berkeley Lab.

The role starts November 1, 2026, is a full-time, two-year appointment with potential renewal, and involves collaboration across multi-institution teams to advance AI-driven discovery in catalysis