Postdoctoral Appointee - Machine Learning for Accelerator Control

Argonne National Laboratory

Lemont, IL

Job posting number: #7071670 (Ref:408968)

Posted: October 29, 2020

Application Deadline: Open Until Filled

Job Description

The Accelerator Operations and Physics (AOP) Group of the Accelerator Systems Division (ASD), Advanced Photon Source (APS) is hiring a postdoc researcher for an appointment of up to three years. The postdoc researcher will perform studies on developing advanced ML methods for accelerator tuning and control, as well as beam diagnostics, with applications to storage ring commissioning and operation. Experimental work on the existing APS accelerator complex and simulations for the future APS Upgrade will be done to test the methods and for developing operation software. The research work is supported by a recently funded DOE ML R&D project.

Control of modern complex accelerators at large scale scientific user facilities has become increasingly more challenging as new machines push technology limiting in the quest for higher performance. Using beam-based diagnostics to identify machine errors that affect the performance will be critical in realizing the design performance of the future machines, as will tuning available control parameters to resolve performance issues and maintain high performance. In this ML R&D project, we will apply existing methods to important tuning and control problems on storage rings. We will explore and develop more efficient and more robust methods to address significant challenges and improve the state of the art. Studies will also be conducted on using ML methods to analyze operational history data in order to extract information to inform accelerator maintenance schedules, as well as to detect and predict component failures. We will collaborate with ML researchers at SLAC and Brookhaven National Laboratories.

Qualifications:
Recent PhD degree in accelerator physics· or another of the physical science is preferred, but strong candidates in applied mathematics, computer science, and electrical engineering who have an interest in accelerator physics will also be considered.
· Strong programming skills
· Proficiency in programming languages Python, Matlab, or C/C++

Preferred Skills and experience:

  • Machine learning and accelerator operation
  • Working with complex algorithms
  • Accelerator modeling and simulation codes, such as Elegant, MAD, or AT
  • Programming w/ SDDS and Tcl/Tk

As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.



Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.


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