Margaret Butler Fellowship – Argonne Scholar

Lemont, IL USA

Full time

423595

The Argonne Leadership Computing Facility's (ALCF) Margaret Butler Fellowship in Computational Science is an exciting and unique opportunity to pursue high-impact scientific research at the intersection of Artificial Intelligence (AI) for Science and High-Performance Computing (HPC) to drive scientific discoveries. The mission of this fellowship is to broaden domain science in leadership-class computing.

This postdoctoral fellow is expected to leverage the ALCF’s world-class computing resources. These include the Aurora Supercomputer — one of the two exascale computing platforms in the US, — and novel AI accelerators in the AI Testbed. The postdoctoral fellow will work closely with multidisciplinary science teams to drive a research agenda and help chart the future direction of scientific computing and artificial intelligence on leadership-class supercomputing facilities. The responsibilities will be determined based on the accepted research proposal and plan and based on meetings with the supervisor/mentor of this fellow.

The appointment is for one year and may be renewed for another year, subject to performance, project needs, and funding availability. The benefits of the appointment include a highly competitive salary and a generous professional allowance. For additional details, including past recipients, see: https://www.alcf.anl.gov/margaret-butler-fellowship-computational-science

This position requires some on-site presence at the Argonne campus in Lemont, Illinois, with the possibility of a hybrid schedule which applies to employees regularly scheduled for some onsite and some remote days, with employees typically working up to 40% of their time remotely.

Position Requirements

• Ph.D. (completed within the last 0-5 years, or to be awarded by the start of the appointment) in applied mathematics, physics, chemistry, computer science, or similar field.

• Knowledge of high-performance computational science. Examples include, but are not limited to computational science research in at least one of the following:

• Biology; chemistry; earth science; engineering; materials science; nuclear energy; physics; renewable energy; energy science; artificial intelligence; numerical methods & applied mathematics

• Experience with at least one AI-driven approach, such as distributed foundation model development and Fine tuning; agentic AI orchestration; development and application of surrogate models; AI integration with autonomous laboratories; multimodal AI; etc.

• Strong analytical and problem-solving skills to contribute to creative solutions to complex problems

• Effective verbal and written communications skills

• Demonstrated ability to collaborate effectively with researchers and contribute to impactful research outcomes

• Ability to work effectively with collaborators within Argonne and at other laboratories, universities, computing centers, and industry organizations.

• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

Job Family

Argonne Scholar

Job Profile

Argonne Scholar

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

The expected hiring range for this position is $85,781.00-$142,968.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

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