Position Description

• We invest in our employees by offering a total rewards package you can count on:
• Exceptional health and retirement benefits, including pension or 401K-style plans
• A culture where you’ll belong - we are invested in our teams!
• In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year.
• Parental bonding leave (for both mothers and fathers)

• Conduct independent and collaborative research to develop new mathematics and algorithms for analyzing complex data from DOE experimental facilities.
• Develop new mathematical algorithms targeting one or more focus areas: (1) 3D+ reconstruction of structure/heterogeneity/dynamics from scattering and/or microscopy data; (2) autonomous analysis and decision-making for self-driving, autonomous experiments; (3) agentic-based methods for extracting patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms that enable new reliable applications of machine learning and artificial intelligence to experimental data analysis.
• Make advances in one or more of: multimodal data fusion/joint inference; uncertainty quantification with realistic noise/measurement error; complex physics- and artifact-aware forward modeling; and theory-grounded guarantees for proposed algorithms.
• Collaborate with scientific users and experimentalists at DOE experimental facilities to apply the developed software to real datasets and meet their scientific needs.
• Publish results in peer-reviewed venues, present at conferences/workshops, and contribute to CAMERA’s collaborative research activities.

• Ph.D. in Applied Mathematics, Computer Science, Physics, or related field.
• Strong research track record developing advanced mathematical and computational methods for analyzing complex experimental or imaging data.
• Demonstrated expertise in several of the following areas: inverse problems, statistics, optimization, uncertainty quantification, LLM, VLM, MCP, and/or agent-based machine learning .
• Strong foundation in at least one of: numerical linear algebra, Fourier/spectral methods, scientific computing, and/or high-performance computing.
• Proven ability to publish in peer-reviewed venues and present research at seminars, workshops, and scientific conferences.
• Excellent written and verbal communication skills, with the ability to contribute effectively to large, collaborative, multidisciplinary projects in a diverse environment.

• Familiarity with modern machine learning methods and software, including experience applying them to scientific or experimental datasets.
• Experience collaborating with domain scientists to analyze real experimental data and translate scientific questions into robust, actionable computational approaches.

• 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: The monthly salary range for this position is $8,827 - $10,234 and is expected to start at $8,570 or above. 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 may be performed on-site, hybrid. The primary location for this role is Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work must be performed within the United States.
• Union Represented: This position is represented by a union for collective bargaining purposes.