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Postdoctoral Appointee - Machine Learning for X-ray Imaging

Argonne

Job Description


The X-ray Imaging group (IMG) of the Advanced Photon Source (APS) is currently seeking a postdoc with expertise in machine learning (ML) and image processing to develop cutting edge processing pipelines for enhanced fidelity ultrahigh-speed imaging.

The successful candidate will be part of a dynamic team, working within a group of scientists conducting a wide range of research using state-of-the-art tools. The IMG group 3 beamlines with a broad set of world-leading full-field imaging instruments, including ultrahigh-speed imaging. The IMG group develops end-to-end scientific software, data analysis and interpretation methods. These instruments and methods support APS user scientific programs and independent beamline scientist research activities in very broad fields, including materials science, geology and biology.

In ultrahigh-speed imaging at 32-ID, we ubiquitously face scientific demand for the highest acquisition frequency (10 MHz), largest field-of-view, and highest spatial resolution (100 nm) simultaneously, an experimental trilemma necessitating compromise for each experiment. The focus of this role will be on advancing an (ML-)enhanced X-ray image fusion, combining simultaneously acquired high and a low-resolution images, to significantly expand our experimental envelope and realize a leap in ultrahigh-speed full-field imaging capabilities.

Position Requirements

Education and Experience Requirements:

  • Recent or soon-to-be completed PhD (typically completed within the last 0-3 years), engineering, materials science, physics or a related scientific field with an emphasis on machine learning and/or image processing.
  • Considerable experience in experimental data analysis including creativity in building new computational algorithms
  • Expertise in Python or other scientific programming languages
  • Excellent oral and written communication skills.
  • Good collaboration skills and be able to work with a team.

Desired but not required knowledge:

  • Experience one of the following: Keras, Pytorch, Tensorflow
  • Experience in software development
  • Experience with synchrotron light source / x-ray free electron laser experiments.

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

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.


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