美国阿贡国家实验室2023年招聘博士后职位（物理/机器学习与人工智能）
美国阿贡国家实验室（Argonne National Laboratory，简称ANL）是美国政府最早建立的国家实验室，也是美国最大的科学与工程研究实验室之一——在美国中西部为最大。阿贡前身是芝加哥大学的冶金实验室 (Metallurgical Lab)，现在隶属于美国能源部和芝加哥大学。诺贝尔物理学奖得主费米于1942年在此领导小组建立了人类第一台可控核反应堆（芝加哥一号堆，Chicago Pile-1)，完成了曼哈顿计划的重要一环，并且使人类从此迈入原子能时代。
Postdoctoral Appointee
Argonne National Laboratory
Job Description
About our Physics Division, ATLAS team:
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.
Advisers and Contact Information:
Brahim Mustapha, Accelerator Physicist, Physics Division, ANL, brahim@anl.gov
Position Requirements
Skills & Experience:
· PhD in physics or engineering or related field
· Strong background in developing and using computer models.
· Familiarity with accelerator operations
· Basic knowledge in machine learning and artificial intelligence techniques are highly desirable.
· Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.
PhD must have been achieved within the last 3 years or with an upcoming defense date
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.
