Postdoctoral Appointee

Argonne National Laboratory

Lemont, IL

Job posting number: #7071932 (Ref:409043)

Posted: November 6, 2020

Application Deadline: Open Until Filled

Job Description

The Argonne Tandem Linear Accelerator System (ATLAS) is the DOE/NP User Facility for the study of low energy nuclear physics with heavy ions. It operates ~6000 hours per year. While capable of delivering high intensities (up to ~1 pµA) of any available stable beam, the facility can also provide low intensity (103 – 106 particles per second) radioactive ion beams (RIB) from the Californium Rare Isotope Breeder Upgrade (CARIBU) source or via the in-flight process using the Argonne in-flight radioactive ion separator (RAISOR). The facility uses 3 ion sources and services 6 target areas at energies from ~1- 15 MeV/u. To accommodate the total number of approved experiments along with their wide range of beam-related requirements, ATLAS reconfigures once or twice per week over 40 weeks of operation per year. The startup time varies from ~12 – 48 hours depending on the complexity, which will increase as the upcoming Multi-User Upgrade project is implemented over the next ~3 years to deliver beam to two experimental stations simultaneously. The use of machine learning and artificial intelligence has the potential of significantly reducing the time needed to tune the accelerator, and improve beam quality with the installation of new diagnostics and real-time data acquisition. These improvements will increase the scientific throughput of the facility and the quality of the data collected. The AI/ML developments proposed in this project will be very beneficial to similar facilities and to the accelerator physics community at large.

The project is seeking a qualified post-doctoral appointee with a PhD in accelerator physics and strong background in developing and using computer models for accelerators. Familiarity with accelerator operations and basic knowledge in machine learning and artificial intelligence techniques are strongly desired.

The post-doctoral appointee will have the opportunity to work with cutting-edge computing platforms for developing, testing and deploying AI/ML approaches with Argonne Leadership Computing Facility (ALCF) and the Data Science and Learning divisions.

Advisers and Contact Information:

Brahim Mustapha, Accelerator Physicist, Physics Division, ANL, brahim@anl.gov

Arvind Ramanathan, Computer Scientist, Data Science & Learning Division, ANL, ramanathana@anl.gov

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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