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Argonne National Laboratory Postdoctoral Appointee - AI for Science in Lemont, Illinois

Postdoctoral Appointee - AI for Science

Requisition Number: 409372 Location: Lemont, IL

Functional Area: Research and Development Division: DSL-Data Science and Learning Division

Employment Category: Temporary 6 Months or Greater Education Required: Not Indicated

Level (Grade): 700 Shift: 8:30 - 5:00 Share: Facebook LinkedIn Twitter

Argonne National Laboratory invites applications from outstanding candidates for multiple postdoctoral research positions in applying artificial intelligence (AI) and machine learning / deep learning (ML/DL) methods to scientific problems as well as for research in foundational aspects of AI. Areas of interest include, but are not limited to, the following:

  • AI-driven discovery and design
  • Goal-driven design of materials, chemicals, proteins, and organisms
  • AI for understanding complex systems and ecologies
  • Self-driving robotic laboratories
  • AI-based program synthesis and automated software generation
  • AI-augmented control for accelerators and science facilities
  • Intelligent sensing and reactive modeling
  • Predictive crisis response and proactive decision support
  • Rare-events analysis and understanding
  • AI-driven manufacturing scale-up
  • Foundational research in AI — mathematical, statistical, and information theoretic methods to advance AI for Science; development of new scalable AI methods and software frameworks.

Candidates will have the opportunity to work on their own research projects that are synergistic with other efforts at Argonne as well as be mentored and advised by Argonne scientists so as to form connections and collaboration opportunities with Argonne research efforts in this space. Candidates will have the opportunity to run on advanced supercomputing facilities at the Argonne Leadership Computing Facility (ALCF), including the upcoming Aurora exascale system, other GPU-based systems, as well as specialized AI hardware from vendors such as Cerebras, SambaNova, Graphcore, and Groq.

A recent PhD in computer science, physical sciences, engineering, or related field; experience in programming with one or more programming languages, such as C, C++, or Python; experience with machine learning methods and deep learning frameworks, such as TensorFlow or PyTorch; knowledge of software development practices and techniques for computational and data-intensive science problems; ability to conduct interdisciplinary research combining physical sciences / engineering and computing; strong collaboration skills and ability to work in a team environment; ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Candidates are asked to submit a two-page statement of research interests along with their application.

The positions are available immediately, but there is flexibility in start dates for highly qualified candidates.

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.