Research Associate - ML​/AI

SLAC Job Postings

The SLAC National Accelerator Laboratory (SLAC) is seeking a Research Associate (RA) to work on Machine Learning (ML) and Artificial Intelligence (AI) for high energy physics (HEP) in connection with the TREASURE DOE HEP American Science Cloud Intelligent Data Pilot and the ATLAS experiment at the Large Hadron Collider (LHC) at CERN.

TREASURE is a multi-DOE laboratory and multi-experiment HEP energy‑frontier collaborative effort that is developing AI‑ready data and using such data to develop and study data tokenization, multi‑experiment AI model training, and explore foundation models for fundamental physics.

The SLAC ATLAS group consists of approximately 20 people, of which 5 are currently based ormation on the SLAC ATLAS group can be found on our web page (Use the "Apply for this Job" box below)..

• ATLAS Physics Analysis, including SM and BSM Higgs and di‑Higgs physics
• Machine learning applications in simulation, reconstruction, trigger, and analysis
• Reconstruction & Performance, including b‑tagging, tracking, jets and jet substructure
• High‑luminosity upgrade, including the Inner Tracker (ITk) upgrade and physics studies
• Detector R&D, including targeted studies for experiments at future colliders

The successful candidate will be based either at SLAC or CERN. The potential start time for this position is Summer 2026. The position is initially a two‑year term with the possibility of renewal.

The successful candidate will contribute to the TREASURE project by developing data tokenization methods, delivering AI‑ready data to the American Science Cloud, developing multi‑experiment training and fine‑tuning methods, and exploring multi‑experiment foundation models. The candidate will also help develop LHC and other experimental open datasets for use in TREASURE, and may engage with physics analysis and related tasks such as simulation, reconstruction, and machine learning for ATLAS/LHC data, as well as other research opportunities in the SLAC ATLAS group.

These are highly competitive positions as part of the general research associate program at SLAC, requiring a background of demonstrated excellence in research and a recent PhD in experimental particle physics, or a related field, or completion of the requirements for such a degree prior to starting the position.

• Cover letter
• Curriculum Vitae, including selected publications
• Research statement
• At least three reference letters (to be submitted by the reference writers to the AJO site)

For further information, please see  or contact Dr. Michael Kagan (makagannford.edu).

• Effective Decisions:
  Uses job knowledge and solid judgment to make quality decisions in a timely manner.
• Self‑Development:
  Pursues a variety of venues and opportunities to continue learning and developing.
• Dependability:
  Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
• Initiative:
  Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
• Adaptability:
  Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
• Communication:
  Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages.
• Relationships:
  Builds relationships to foster trust, team collaboration, and a positive climate to achieve common goals.

• Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

• Interpersonal
  
  Skills:
  
  Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
• Promote Culture of Safety:
  Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the…