Postdoc: AI-Driven Materials Discovery Scientist

University of Delaware; Berkeley (CA); On-site; USD 86,936 - 97,103; Full time

Benefits offered by this job

Competitive salary

Access to world-class HPC resources

Opportunity for renewal based on performance

Job summary

A leading research institution seeks a Postdoctoral Scholar in AI-Driven Materials Discovery. This role involves developing machine-learning interatomic potentials and collaborating with scientists on cutting-edge projects. Ideal candidates hold a Ph.D. and demonstrate expertise in ML models and DFT methods, along with a strong publication record. The position is on-site at Lawrence Berkeley Lab and offers a competitive salary. Apply by October 1, 2025 to contribute to innovative materials science research.

Qualifications

• Ph.D. in a relevant field required.
• Expertise in training machine learning models for materials science applications.
• Proficiency in Python and HPC environments.

Responsibilities

• Develop, train, and validate ML interatomic potentials.
• Build and optimize DFT workflows for property prediction.
• Collaborate with scientists to tackle materials science problems.

Skills

Training machine learning models

Strong publication record

Independent and collaborative work

Education

Ph.D. in Materials Science, Physics, Chemistry, Computer Science

Tools

Python

DFT methods (VASP, Quantum ESPRESSO, WIEN2k)

MLIP frameworks (NequIP, MACE, CHGNet, GAP, SNAP)

Job description

A leading research institution seeks a Postdoctoral Scholar in AI-Driven Materials Discovery. This role involves developing machine-learning interatomic potentials and collaborating with scientists on cutting-edge projects. Ideal candidates hold a Ph.D. and demonstrate expertise in ML models and DFT methods, along with a strong publication record. The position is on-site at Lawrence Berkeley Lab and offers a competitive salary. Apply by October 1, 2025 to contribute to innovative materials science research.