Job Details

Overview

Lawrence Berkeley National Laboratorys (LBNL) Chemical Sciences Division is seeking a Postdoctoral Fellow in Digital Chemistry and Spectroscopy. The role focuses on the digitalization of theoretical and experimental chemistry protocols to understand reactivity at energy-relevant interfacial systems, such as photo- and electro-catalysis and batteries. A central objective is the development and application of a digital twin approach to bridge theoretical predictions, data interpretation, and experimental/spectroscopic observations.

What You Will Do

• Design & advance digital twins: Integrate electronic structure (e.g., DFT, ab initio MD, tight?binding) with multiscale simulations to predict experimental observables at interfaces.
• Bridge theory & experiment: Co?design validation experiments with experimentalists; iterate models using feedback from new measurements.
• Automate the workflow: Build Python workflows for simulation and data processing, including HPC job orchestration with LLMs/agentic AI.
• Make spectroscopy actionable: Implement/benchmark modules for X?ray, IR, and Raman; document assumptions; quantify sensitivity and uncertainty.
• Publish reproducible science: Curate FAIR datasets and notebooks; maintain internal docs/how?to guides; contribute to peer?reviewed publications and technical reports.
• Collaborate & mentor: Work in a multidisciplinary team with theorists and experimentalists; mentor interns and junior researchers.
• Champion safety & cybersecurity in all collaborative and computational activities.

What Is Required

• Ph.D. in Chemistry, Physics, Materials Science, or a related field.
• Hands-on experience with first-principles or atomistic methods relevant to interfacial systems, including DFT, ab initio MD, enhanced sampling techniques, or tight-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces.
• Working knowledge of machine learning techniques in materials or chemical modelinge.g., graph neural networks, ML interatomic potentials, and uncertainty quantification.
• Strong programming skills in Python (or equivalent), with demonstrated experience in software development.
• Experience applying large language models (LLMs) or autonomous agents to scientific tasks such as code generation, protocol reasoning, or automated experimental planning.
• Proven ability to bridge theory and experiment, particularly in spectroscopy or operando studies.
• Excellent communication and software engineering practices, including proficiency with Git, unit testing, virtual environments, code packaging, and basic CI pipelines.

Notes

• This is a full-time 2-year postdoctoral appointment with the possibility of renewal based on performance, continuing availability of funds, and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience.
• This position is represented by a union for collective bargaining purposes. The monthly salary range is $5,562$9,136 and may start at $7,716+ depending on postdoctoral step rates; salaries are predetermined based on postdoctoral step levels.
• Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. A REAL ID or other acceptable form of identification is required to access Lab sites.
• This position may be subject to a background check. Having a conviction history will not automatically disqualify an applicant.

EEO Statement

Berkeley Lab is an Equal Opportunity Employer. We strive to build a diverse and inclusive community and encourage applications from all qualified individuals regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories. Berkeley Lab is a University of California employer and adheres to affirmative action and anti-discrimination policies as a Federal and State contractor.

Careers: careers.lbl.gov