Postdoc – Deep learning for nanoscale X-ray imaging of materials

Argonne National Laboratory

Lemont, IL

Job posting number: #7071813 (Ref:409016)

Posted: November 3, 2020

Application Deadline: Open Until Filled

Job Description

The Advanced Photon Source (https://www.aps.anl.gov/a>) at Argonne National Laboratory (Chicago, US) invites applicants for a postdoctoral position to develop AI-enabled nanoscale coherent x-ray diffraction imaging methods. Coherent imaging methods (including Ptychography) provide a powerful means of imaging materials at resolution beyond the limits of x-ray optics and under operando i>conditions.

 The successful candidate will lead the development of an end-to-end AI-enabled workflow for data inversion, abstraction and experimental control for scanning coherent imaging (including Ptychography). The end goal is to apply this framework to address some of the most pressing scientific challenges in energy storage and conversion, catalysis and quantum information sciences. The successful candidate will be part of a cross-lab, highly inter-disciplinary team of experts in AI-enabled imaging, advanced optimization, experimental automation, data science, coherent imaging, materials science and X-ray instrumentation.

 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 a background in machine learning, computational materials science, computational physics, image processing, inverse problems, applied math and x-ray science are encouraged to apply.

Relevant knowledge, skills, and abilities include:

  • Skill in programming languages such as Python and machine learning libraries including Tensorflow/PyTorch, scikit-learn etc.
  • Knowledge of computational imaging, particularly coherent diffraction imaging and phase retrieval algorithms.
  • Experience with Git/GitHub.
  • Knowledge of x-ray physics, including x-ray diffraction, scattering processes, etc..
  • Experience in applying deep learning to phase retrieval, image segmentation, image denoising etc.
  • 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 synchrotron/XFEL experiments and/or experimental automation is preferred.

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, 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 Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.



Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.


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