美國阿貢國家實驗室2023年招聘博士后職位（物理/機器學習與人工智能）

美國阿貢國家實驗室（Argonne National Laboratory，簡稱ANL）是美國政府最早建立的國家實驗室，也是美國最大的科學與工程研究實驗室之一——在美國中西部為最大。阿貢前身是芝加哥大學的冶金實驗室 (Metallurgical Lab)，現(xiàn)在隸屬于美國能源部和芝加哥大學。諾貝爾物理學獎得主費米于1942年在此領(lǐng)導(dǎo)小組建立了人類第一臺可控核反應(yīng)堆（芝加哥一號堆，Chicago Pile-1)，完成了曼哈頓計劃的重要一環(huán)，并且使人類從此邁入原子能時代。

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.

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