Postdoctoral Appointee - Computer Scientist

Argonne National Laboratory

Lemont, IL

Job posting number: #7069868 (Ref:5355)

Posted: September 16, 2020

Application Deadline: Open Until Filled

Job Description

Math and Computer Sciences at Argonne National Laboratory provides intellectual and technical leadership in the computing sciences, applied mathematics, computer science, and computational science. To this end, we pursue the most important scientific problems of our nation, problems that require innovative computational tools and technology for transformative science during the next several decades.

The Mathematics and Computer Science Division at Argonne National Laboratory seeks Postdoctoral  candidates for a position in scientific machine learning. The successful candidate will be performing machine learning research and development for scientific discovery on some of the world’s fastest supercomputers. This work will include development of scalable supervised, unsupervised, reinforcement, transfer, meta, continual, and automated learning methods with flexibility on the class of methods pursued based on wide range of DOE relevant scientific applications such as nuclear physics, computational chemistry, materials science, high performance computing, weather modeling, and transportation. You will actively collaborate with computer scientists, mathematicians, and domain scientists and will have the opportunity to build an independent research program.

This is an Evergreen job posting which allows candidates to apply once to be considered for multiple job requisitions; you may be asked to apply to a specific job posting in the future.

  • Recent PhD (obtained within 3 years)
  • Experience in machine/deep learning, high-performance computing, scientific computing or mathematical optimization
  • Programming experience in C, C++, and/or Python
  • Experience in Keras, Tensorflow, Pytorch
  • Good communication skills both verbal and written
  • Software development practices and techniques for computational and data-intensive science problems. 

Desirable knowledge and skills:

  • Ability to understand and implement methods from latest machine learning articles
  • Experience and skills in interdisciplinary research involving computer scientists and discipline scientists
  • Experience or knowledge with parallel programming such as MPI
  • Collaborative skills including the ability to work well with other laboratories and universities, supercomputer centers and industry. 

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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