Postdoctoral Researcher

Lawrence Berkeley National Laboratory • Berkeley, CA

2d • $5,957 - $9,234 • Onsite

About The Position

The Accelerator Physics Group (APG) of the Advanced Light Source (ALS), within the Accelerator Technology & Applied Physics (ATAP) Division at Lawrence Berkeley National Laboratory, seeks a Postdoctoral Researcher to develop artificial intelligence and machine learning methods for accelerator physics and operations. The researcher will combine physics-based simulation, digital twins, surrogate modeling, and statistical analysis of machine data to support optimization, diagnostics, and operational decision-making. Research will examine how models calibrated against machine data and equipped with uncertainty quantification can support reliable AI-driven workflows, tested in simulation and validated through beam studies.

The researcher will integrate models and workflows into Osprey, the ALS agentic AI platform, enabling agents to analyze, optimize, and operate the accelerator under human oversight. The position is part of MOAT-Core, a multi-laboratory DOE Genesis Mission program focused on AI-driven accelerator facilities. In collaboration with the Osprey team and partner laboratories, the researcher will develop and validate shared capabilities for the consortium. The role includes collaboration with laboratories in the United States and abroad and opportunities to present at leading accelerator physics and AI conferences.

The ALS is a DOE Office of Science national scientific user facility producing bright soft and hard x-ray, ultraviolet, and infrared light. It serves thousands of academic and industrial users and is co-located with other Berkeley Lab research facilities and divisions. The ALS is pursuing ALS-U, a major upgrade to a fourth-generation light source. The position also supports relevant ALS-U commissioning activities.

This is a full-time, two-year postdoctoral appointment, with possible renewal based on satisfactory performance, funding availability, and operational needs. Candidates must have less than three years of paid postdoctoral experience. Work will be performed primarily at Lawrence Berkeley National Laboratory in Berkeley, California. The position is subject to a background check and requires an acceptable form of identification to access the Lab site.

Requirements

• Ph.D. in physics, accelerator physics, or a closely related field, awarded within the last three years or expected before the appointment start date.
• Less than three years of paid postdoctoral experience.
• Research experience applying machine learning methods to physics, engineering, or scientific problems.
• Solid knowledge of accelerator physics fundamentals.
• Proficiency in Python and scientific computing, with software engineering skills including version control, testing, and documentation.
• Ability to use AI coding tools effectively and critically, including reviewing and validating generated code.
• Working knowledge of machine learning, statistical inference, and optimization methods.
• Ability to work independently and effectively in a multidisciplinary team of physicists, software engineers, controls engineers, and operations staff.
• Good written and oral communication skills and ability to present research results effectively.
• Ability to foster constructive, respectful, and cooperative workplace relationships.

Nice To Haves

• Experience with charged-particle beam dynamics and accelerator simulation codes such as Elegant, Xsuite, Bmad, ImpactX, or similar.
• Experience with surrogate models, digital twins, or model calibration against experimental data.
• Experience with beam-based measurements at an accelerator facility, preferably a synchrotron light source.
• Experience with machine-learning frameworks such as PyTorch or JAX and differentiable simulation.
• Familiarity with accelerator control systems such as EPICS and archived machine data.
• Experience with LLM-based agents or agentic workflows for scientific facilities.

Responsibilities

• Conduct applied research on machine learning methods for accelerator physics and operations, including digital twins, surrogate modeling, model calibration against machine data, statistical data analysis, and uncertainty quantification.
• Build and maintain digital twins and virtual accelerators of the ALS and ALS-U, connecting physics simulation codes with live and archived machine data.
• Develop and evaluate agentic AI workflows in which agents use digital twins and machine data to plan, carry out, and analyze optimization tasks and beam studies.
• Develop AI-driven operations workflows for optimization, diagnostics, anomaly detection, data analysis, and decision support, and validate them in simulation before machine deployment.
• Carry out beam studies and machine-development shifts to validate models and automated workflows on the ALS.
• Integrate models and operations workflows into Osprey as validated tools for AI agents in accelerator operations.
• Package methods and workflows for adoption by partner facilities in the MOAT-Core consortium.
• Use testing, CI/CD, documentation, and code review to produce reliable, maintainable open-source software.
• Disseminate findings through peer-reviewed publications, conference presentations, workshops, and collaboration meetings.
• Participate in ALS-U commissioning activities related to modeling and AI-driven operations.
• Comply with LBNL/ALS Environment, Safety, and Health (EH&S) practices and requirements.
• Contribute to a respectful and collaborative work environment.

Benefits

• Comprehensive medical, dental, and vision coverage.
• Retirement savings through the UC Defined Contribution Safe Harbor Plan, with additional voluntary UC retirement savings options available.
• 24 days of Personal Time Off (PTO) per fiscal year, plus sick leave and paid holidays.
• Eight weeks of Postdoctoral Paid Family Leave for qualifying family and parental needs.
• Berkeley Lab Postdoc Association opportunities for connection, networking, social events, professional development, and scientific exchange.
• A collaborative and inclusive culture supporting research-career growth and belonging.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree