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Assistant Computational Scientist: Deep Learning for X-ray Science - Military Veterans

at Argonne National Laboratory

The Advanced Photon Source (APS) ( ) at Argonne National Laboratory (Chicago, US) invites applicants for a computational scientist staff position to develop deep learning (DL) methods and tools for x-ray science experiments. At the APS, we are developing DL models for accelerated data analysis, experimental steering and scientific knowledge extraction. X-ray characterization provides a powerful means of studying materials at extreme resolution and under operando conditions but require challenging data handling and computational resources. The successful candidate will lead the development of a program leveraging high-performance computing (HPC) and DL training at scale to address these data and compute challenges.

The successful candidate will be responsible for developing algorithms, scientific software and machine learning (ML) workflows for large-scale x-ray data analysis, including large DL models. They will work closely with and participate in data-intensive experiments. They will be responsible for reporting relevant results in publications and talks at conferences and will maintain cognizance of state of the art techniques and methods in ML and x-ray science.

The successful candidate will be part of a cross-lab, highly inter-disciplinary team of experts in ML, applied math, HPC, signal processing, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including the world's first exascale computer (Aurora) and one of the brightest synchrotron x-ray sources in the world (APSU).

Candidates with hands-on experience developing and deploying DL models at high data rate materials characterization instruments particularly at synchrotrons and XFELs are encouraged to apply. Candidates are encouraged to include a cover letter in addition to a CV.

Position Requirements


  • A Doctorate or equivalent education and experience is required.
  • Knowledge of x-ray/electron/optical physics, including diffraction, optics, detectors, scattering etc
  • Experience with deep learning (DL) libraries such as Tensorflow, PyTorch, JAX etc.
  • Publication record in applying ML to X-ray, electron or optical characterization data.
  • Experience with ML guided experimental data acquisition.
  • Role model Argonne's Core Values.
  • Understand, value, and promote diversity.

  • Experience in experimental technique development related to x-ray, electron or optical characterization.
  • Skill in programming in Python and one other language (C/C++, Go, Rust etc).
  • Experience with version control such as Git and collaborative software development.
  • Experience with training DL models at scale.
  • Skill in written and oral communications. Experience interacting with scientific staff and research groups. Ability to work effectively as a member of a team. Ability to effectively communicate with people of diverse backgrounds and skill sets.
  • Experience with computational modeling packages related to x-ray, electron or optical characterization.

Job Family
Research Development (RD)

Job Profile
Computational Science 2

Worker Type

Time Type
Full time

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.

Lemont, IL

Argonne National Laboratory

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