Postdoctoral Research Associate - Machine Learning in Energy Physics Detector Operations

Postdoctoral Research Associate - Machine Learning in High Energy Physics Detector Operations

Argonne National Laboratory, HEP High Energy Physics Division

Position Title:

Postdoctoral Research Associate

Position Location:

Lemont, Illinois 60439, United States

Subject Area:
High Energy Physics / Machine Learning

Appl Deadline: none (posted 2025/11/14)

The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in High‑Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time‑series modeling, and clustering algorithms.

The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory.

The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division, designing and executing ML experiments on leadership‑class computing facilities such as the Aurora and Polaris supercomputers. Argonne is a multidisciplinary national laboratory and offers an exciting campus atmosphere in which to collaborate on interdisciplinary projects, at both the national and international levels.

The initial appointment will be for one year, with possibility of renewal for another two years and is based at Argonne National Laboratory (Lemont, Illinois).

• Recent or soon‑to‑be completed PhD (within the last 0‑5 years) in high energy physics, or a related field
• Experience with applying unsupervised ML algorithms such as autoencoders, clustering, to time‑series data is preferred
• Experience with the data from HEP experiments is strongly required
• Programming expertise in Python and either PyTorch or Tensor Flow is required
• Experience using High‑Performance Computers (HPCs) is preferred
• Ability to model Argonne’s Core Values:
  Impact, Safety, Respect, Integrity, and Teamwork

• Curriculum vitae
• A description of research interests
• Three letters of recommendation

Openings are available immediately; there is flexibility in starting dates for highly qualified candidates. Review of applications will begin immediately and applications that are received by December 1, 2025, will receive priority. The positions will remain open until filled.

For further information and questions please contact Walter Hopkins at whopkins.

Application instructions:
Send complete applications to HEPHR. The job posting can be found on the Argonne Careers website: