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SLAC National Accelerator Laboratory is one of 17 Department of Energy (DOE) National Laboratories, and operated by Stanford University on behalf of the DOE. SLAC develops and operates some of the world’s premier science facilities, including the first hard X-ray free-electron laser. Research at SLAC explores the structure and function of matter and the properties of energy, space and time, at the smallest and largest scales, all with the goal of solving problems facing society and advancing human knowledge.



Research Associate for Beam-based Optimization (BBO) and Machine Learning (ML)

Job Requisition #: 3375
Classification Title: Research Associate - Experimental
Grade: NA
# of openings: 1
FLSA: Exempt
Location: Menlo Park, CA (HQ)


The SPEAR3 AP Group at SLAC National Accelerator Laboratory has opened a Research Associate position for R&D in beam based optimization and machine learning for synchrotrons. This position is a two-year appointment funded by the Advanced Scientific Computing Research, Department of Energy. We look for motivated candidates who are recent PhD graduates in accelerator physics or related disciplines and have a strong background in math and computer science.

 The R&D work will explore and develop effective and efficient methods for online optimization of complex, computer-controlled systems, including the use of machine learning techniques. The work will also include the investigation of using machine learning techniques to expedite the design optimization of synchrotron light sources. Simulation and experimental studies will be conducted to test and verify the effectiveness of the methods. The successful candidate will collaborate with ML experts at SLAC and LBL on this project.


  • PhD degree in accelerator physics or related fields
  • Recent PhD graduates in other branches of physics, or in math, computer science, and electric engineering who have demonstrated knowledge in mathematical optimization and are interested in accelerator physics will also be considered.
  • Strong mathematics and analytic ability is essential.
  • Strong programming skills
  • Proficiency in programming languages Matlab and/or Python

Desired Skills:

  • Previous experiences with machine learning and accelerator operation are a plus.
  • Previous experience working with complex algorithms is preferred.

Applicants should include a cover letter, a CV with a list of publications and names of three references for future letters of recommendation with the application. Potential applicants who wish to discuss the position in more detail may contact the hiring manager Dr. Xiaobiao Huang at

 SLAC Competencies:

  • Effective Decisions:  Uses job knowledge and solid judgment to make quality decisions in a timely manner.
  • Self-Development:  Pursues a variety of venues and opportunities to continue learning and developing.
  • Dependability:  Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
  • Initiative:  Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
  • Adaptability:  Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
  • Communication:   Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages.
  • Relationships:  Builds relationships to foster trust, collaboration, and a positive climate to achieve common goals. 


  • Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
  • Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1—General Policy and Responsibilities:
  • Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide,


SLAC National Accelerator Laboratory is an Affirmative Action / Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All staff at SLAC National Accelerator Laboratory must be able to demonstrate the legal right to work in the United States. SLAC is an E-Verify employer.


Final candidates are subject to background checks prior to commencement of employment at the SLAC National Accelerator Laboratory.

Internal candidates, who are selected for hire, may require degree verification and/or credit checks based on requirements of the new position.


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