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Postdoctoral Appointee Simulating Particle Physics Detectors, Machine Learning, Exascale
Requisition Number: 409292 Location: Lemont, IL
Functional Area: Research and Development Division: LCF-Leadership Computing Facility
Employment Category: Temporary 6 Months or Greater Education Required: Not Indicated
Level (Grade): 700 Shift: 8:30 - 5:00
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The Argonne Leadership Computing Facility (ALCF) has an immediate opening for a postdoctoral researcher to work on novel data-driven scientific discoveries targeting our forthcoming exascale supercomputer, Aurora. ALCF hosts some of the world’s most powerful supercomputers. As a DOE User Facility, the ALCF provides supercomputing resources for many scientists around the world.

You will collaborate with scientists in the Early Science Program (ESP) which aims to prepare scientific software to use on America’s first Exascale supercomputer, Aurora, to be deployed in 2022. The program links multi-institutional investigator teams with ALCF staff and Aurora vendor applications experts.

The successful candidate will work with a team of scientists from the ATLAS Experiment, located at CERN in Geneva, Switzerland, who are adapting experimental particle reconstruction and simulation algorithms for the future Aurora supercomputer. Candidates will apply machine learning and vectorized algorithms on leading edge architectures from industry chip makers to help address the challenges posed by data-intensive calculations such as those of the ATLAS experiment.

You will join the ALCF Data Science Group, a multi-disciplinary team of scientists and high- performance computing (HPC) software engineers, working with large scientific datasets to carry out advanced analytical methods using statistical analysis and machine learning techniques.



While all candidates will be considered, the ideal candidate will have the following qualifications:

  • PhD in Computer Science, Physics, or another related field.
  • Comprehensive experience programming in one or more programming languages such as C, C++, and Python.
  • Comprehensive knowledge of parallel algorithms, machine learning frameworks, vectorized algorithms, and/or high-performance computing frameworks.
  • Experience in software performance analysis and engineering.
  • Independent judgment and critical thinking.
  • Strong communication skills.
  • Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

 Application Documents:

  • Cover letter (optional); uploaded as a PDF document.
  • Resume
  • 3 Letters of Recommendation

Questions can be addressed to Taylor Childers: jchilders@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, gender expression, 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 Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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