Scientific Software Postdoc for Autonomous Spectromicroscopy

Scientific Software Postdoc for Autonomous Spectromicroscopy The Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory is seeking a Scientific Software Postdoc for Autonomous Spectromicroscopy to collaborate with an interdisciplinary team of scientists to perform original research and develop new automation capabilities for spectromicroscopy beamlines at the Advanced Light Source (ALS) by implementing machine learning-based sample screening and agentic AI-driven instrument control. The work will include developing and validating ML methods to identify high-value sample regions in real time; developing an agentic AI component to enable automated instrument control; and evaluating system performance across a variety of experimental scenarios. The position will also entail communicating research results through peer-reviewed publications and scientific presentations, with the goal of enabling autonomous, intent-driven experimentation at x-ray spectromicroscopy beamlines. The Advanced Light Source is a U.S. Department of Energy (DOE) Office of Science national scientific user facility that produces exceptionally bright soft and hard x-ray, ultraviolet, and infrared light. With a strong scientific reputation, expert staff, and advanced capabilities, the ALS attracts thousands of academic and industrial users each year in condensed matter and quantum materials, energy sciences, biosciences, earth and planetary sciences and more. The ALS is one of five Berkeley Lab user facilities that serve 15,000 users annually. Co-located with the Molecular Foundry, NERSC supercomputing center, and Berkeley Lab's materials, chemical sciences, biosciences, and other divisions, it provides an ideal collaborative environment for innovative scientific discoveries. The ALS is a global leader in soft x-ray science, and aims to maintain its leadership with ALS-U, a major project to upgrade the facility to a fourth-generation light source. This upgrade will position the facility among the brightest soft x-ray light sources in the world, offering capabilities that no other facility can provide. Following the ALS Upgrade, our beamlines will benefit from dramatically increased coherent soft x-ray flux, enabling microscale x-ray reflectometry to reach its full potential while already delivering impactful scientific results today. You will: Investigate and validate machine learning or computational methods for real-time analysis of X-ray spectromicroscopy data at an ALS beamline. Create/extend an agentic component using modern AI techniques (tool and skill use, vision-language models, task planning) that interprets multimodal scientific input (images, text, etc.) to formulate instrument commands. Integrate this agentic component with real-time analysis results and beamline control into a unified system, and characterize its performance across a variety of experimental scenarios. Participate in experimental planning, commissioning, troubleshooting, and data-quality assessment associated with deploying new automation capabilities at beamlines. Apply appropriate human-in-the-loop, validation, and fail-safe approaches when developing AI-enabled experimental control workflows. Collaborate closely with scientists, engineers, and technical support staff at the ALS and with external academic collaborators. Contribute to existing open source scientific software and release relevant project outputs where appropriate. Present research findings at scientific meetings, conferences, and publish in peer-reviewed journals. Required Qualifications: PhD degree in the Physical Sciences, Material Science, Applied Mathematics, Electrical Engineering, Computer Science, or related discipline. Experience and a strong interest in scientific software development or research software engineering. Knowledge of machine learning principles and practices. Familiarity with agentic AI systems and concepts. Ability to work collaboratively with a diverse team of scientists and engineers. Demonstrated record of scientific excellence through publications, talks, or software deliverables. Commitment to collaborative software development practices: version control and code review, unit testing, and continuous integration. Strong written and verbal communication skills, including the ability to write publications and present research findings. Desired skills/knowledge: Proficiency in Python and the open source scientific Python software stack. Experience with hyperspectral or multidimensional imaging data, spectral unmixing, or inverse problems. Experience with one or more spectroscopy or spectromicroscopy techniques (e.g. STXM, ptychography, XRF, EDS, EELS, IR, etc.) and interpretation of spectra. Experience translating scientific objectives into software requirements, computational workflows, or system interfaces. Experience building applications with large language models, including use of or contribution to agentic frameworks, tool/function calling, prompt engineering, and context management. Experience developing evaluation or benchmarking frameworks for ML or agentic AI systems. Additional information: Application date: Priority consideration will be given to candidates who apply by September 29, 2026 . Applications will be accepted until the job posting is removed. Appointment type: This is a full-time 2-year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree. Salary range: This position is represented by a union for collective bargaining purposes. The salary range for this position is $99,192 - $110,808. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral experience. Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment. Work modality: 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 Berkeley Lab sites (for more information click here ). Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law. Misconduct Disclosure Requirement: As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer. For additional information, click here . #J-18808-Ljbffr Lawrence Berkeley National Laboratory