Bruno Rossi Distinguished Postdoctoral Fellowship (Senior Scientist)

Mission Support and Test Services, LLC (MSTS) manages and operates the Nevada National Security Site (NNSS) for the U.S. National Nuclear Security Administration (NNSA).  Our MISSION is to help ensure the security of the United States and its allies by providing high-hazard experimentation and incident response capabilities through operations, engineering, education, field, and integration services and by acting as environmental stewards to the Site’s Cold War legacy.  Our VISION is to be the user site of choice for large-scale, high-hazard, national security experimentation, with premier facilities and capabilities below ground, on the ground, and in the air. (See NNSS.gov for our unique capabilities.) Our 2,750+ professional, craft, and support employees are called upon to innovate, collaborate, and deliver on some of the more difficult nuclear security challenges facing the world today.   

• MSTS offers our full-time employees highly competitive salaries and benefits packages including medical, dental, and vision; both a pension and a 401k; paid time off and 96 hours of paid holidays; relocation (if located more than 75 miles from work location); tuition assistance and reimbursement; and more.  
• MSTS is a limited liability company consisting of Honeywell International Inc. (Honeywell), Jacobs Engineering Group Inc. (Jacobs), and HII Nuclear Inc.

The NNSS Science & Technology Directorate invites exceptional early‑career scientists to apply for the Bruno Rossi Distinguished Postdoctoral Fellowship. The Rossi Fellow will drive advances in the theory, computational modeling, and/or machine‑learning applications to the Scorpius linear induction accelerator (LIA) - one of the Nation’s flagship capabilities supporting NNSA missions. This is a high‑impact, publication‑friendly role embedded with a senior NNSS mentor and collaborating laboratories.

Fellowship Focus Areas (Theory/Computation/ML)

We are seeking exceptional early-career PhD scientists or engineers with expertise in accelerator physics and a passion for applying data science, artificial intelligence (AI), and machine learning (ML) to model, control, and optimize complex systems. We also welcome applicants with a strong background in computational science who are eager to apply their skills to challenges in accelerator science. In particular, fellows will be fully supported to lead a research program focused on one or more of the following areas.

A. Accelerator & Beam Physics (Theory/Computation)

·        Relativistic Beam–Target Interaction Physics: Investigating beam interactions with complex bremsstrahlung converters, including X-ray source modeling, dose and fluence optimization, and converter survivability under advanced material responses.

·        Beam Transport in Complex Environments: Studying beam transport in solenoidal and induction systems, with emphasis on emittance preservation, halo formation and mitigation, and energy spread control under realistic operating conditions.

·        Collective Effects and Instabilities: Developing models and mitigation strategies for phenomena such as Beam Breakup (BBU), corkscrew motion, and transverse/longitudinal impedance-driven instabilities.

• Pulsed-Power and Accelerator Coupling: Exploring circuit-beam co-simulation, magnet and induction module dynamics, and timing and waveform shaping to enhance stability and brightness.

• Diagnostics by Design: Creating inference methods and synthetic diagnostics to extract critical machine parameters—such as emittance, current, energy, spot size, and centroid motion—from limited data, while incorporating uncertainty quantification and error budgets for machine studies.

  ·       Multiphysics Target Response: Modeling the Magneto-Hydrodynamics (MHD) and thermomechanics of converter materials under intense pulsed loading, including shock and thermal fatigue, to evaluate lifetime and performance trade-offs.

Representative tools and methods: Particle-In-Cell (PIC) and Vlasov–Fokker–Planck simulations, hybrid PIC–fluid models, envelope and moment techniques, Monte Carlo radiation transport, surrogate modeling, adjoint and gradient‑based optimization, as well as rigorous uncertainty quantificat