Postdoctoral Appointee - Collaborative Machine Learning Platform for Scientific Discovery

Argonne National Laboratory

Lemont, IL

Job posting number: #7075220 (Ref:409363)

Posted: February 17, 2021

Application Deadline: Open Until Filled

Job Description

The X-Ray Science Division (XSD) at the Advanced Photon Source (APS) (https://www.aps.anl.gov/) at Argonne National Laboratory invites applicants for a postdoctoral position to develop a machine learning platform for scientific discovery at scientific user facilities. This shared platform will lower the barrier to entry to machine learning driven discovery by leveraging advances in machine learning methods across user facilities, thus empowering domain scientists and data scientists to make new scientific discoveries using existing and new data. This position will be funded under a 3-year Department of Energy award: A Collaborative Machine Learning Platform for Scientific Discovery. The selected candidate will work as a part of a multidisciplinary research team comprised of scientists from several National Laboratories.

The successful candidate will perform R&D and other activities to collaboratively develop a framework to share and distribute machine learning models/data/networks among the user community. This framework will capture, store, and track contributions, and enable users to search contributions. The candidate will lead the deployment of these tools at an APS beamline to advance discovery in x-ray data analysis.

We are seeking a combination of the following knowledge, skills, and experience:

  • Comprehensive programming proficiency, preferably in Python.
  • Experience with machine learning methods and frameworks especially applied to physical science problems.
  • Experience with x-ray data analysis and/or modeling, such as crystallography, diffraction, or spectroscopy data analysis and/or modeling.
  • Skill in written and oral communications.
  • Ability to work as part of a team to solve problems of scientific and technological interest to the APS.
  • The selected candidate should hold a Ph.D. before starting this position.


Preferred experience:

  • Experience with synchrotron light source / x-ray free electron laser experiments.
  • Experience using high-performance computing systems and facilities.


This level of knowledge is typically achieved through a formal education in at the PhD level in the physical sciences, computer science or engineering, or a related field.

 

Questions about this position should be directed to nschwarz@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, 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.



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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