Physical Science Research Professional 1

Stanfordlivetickets; Menlo Park (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

SLAC National Accelerator Laboratory seeks an AI Physics Research Professional to advance ML and AI for high energy physics, collaborating on the TREASURE project and with ATLAS at the LHC. You will help create AI-ready data, tokenization strategies, and multi-experiment training and fine-tuning methods, enabling foundation models for physics.

Based at SLAC or CERN, the role starts Fall 2026 and focuses on data-centric science, simulation, and analysis tasks, contributing to open datasets and

Qualifications

• Experience applying AI/ML to experimental particle physics.
• Experience with data tokenization and AI-ready data pipelines.
• Familiarity with LHC data and ATLAS analyses.

Responsibilities

• Develop data tokenization methods and deliver AI-ready data for TREASURE and ATLAS analyses.
• Develop multi-experiment training and fine-tuning methods for foundation models in physics.
• Engage with physics analysis, simulation, reconstruction, and ML tasks for ATLAS/LHC data.

Skills

AI/ML in HEP

Data tokenization

Job description

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

TREASURE is 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 and multi-experiment AI model training, and explore foundation models for fundamental physics.

The successful candidate will be based either at SLAC or CERN. The potential start time for this position is Fall 2026.

Position Responsibilities:

The successful candidate will contribute to the TREASURE project, including developing data tokenization methods and delivering AI-ready data to the American science cloud, developing multi-experiment training and fine-tuning methods, and exploring multi-experiment foundation models. The successful will also help develop LHC and other experimental open datasets for use in TREASURE. The successful candidate may also engage with physics analysis and related tasks such as simulation, reconstruction, and machine learning for ATLAS / LHC data, and other research opportunities in the SLAC ATLAS group, while also demonstrating alignment with the SLAC mission and values (https://careers.slac.stanford.edu/working-slac/mission-vision-values).

Qualifications:

These are highly competitive positions, requiring a background of demonstrated relevant experience in AI/ML for experimental particle physics, or related field.