Postdoctoral Researcher (Accelerator Physics) - RF Linac Design & AI/ML-Based Operations

Argonne National Laboratory • Lemont, IL

2d

About The Position

In 2014, our team developed a compact proton linear accelerator (linac) design for an accelerator-driven system (ADS) aimed at nuclear waste transmutation, based on state-of-the-art niobium superconducting RF (SRF) technology. Since then, a transformative SRF approach using Nb₃Sn has emerged, offering performance comparable to niobium while enabling operation at higher temperatures—potentially reducing operating costs for future linac-based accelerator facilities. Under a newly approved project, we will redesign the original ADS driver linac to leverage Nb₃Sn SRF technology.

Requirements

• Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of accelerator physics or a closely related science and engineering discipline
• Strong experience developing and applying computational modeling and simulation
• The successful candidate will demonstrate expertise in accelerator physics, accelerator design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

Nice To Haves

• Familiarity with accelerator operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable

Responsibilities

• Leading the physics design of a next-generation proton linac, optimizing acceleration efficiency and transverse focusing
• Designing new SRF accelerating cavity types across multiple operating frequencies
• Conducting end-to-end beam dynamics studies at full beam energy and current, including comprehensive error analyses using large-scale beam simulations
• Evaluating linac failure scenarios, defining tolerable failure modes, and developing strategies and procedures for rapid recovery with realistic recovery-time targets
• Developing AI/ML-based tools to enable fast accelerator tuning and automated recovery following component failures
• Experimentally validating the AI/ML methods on the ATLAS linac at Argonne National Laboratory

Benefits

• comprehensive benefits are part of the total rewards package
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees