Spallation Physics & Target Neutronics - Postdoctoral Researcher

What You Will Do

Join the Radiation Effects & Radiography team supporting operations at the Los Alamos Neutron Science Center (LANSCE), a premier experimental facility at Los Alamos National Laboratory (LANL). LANSCE's high-power linear accelerator drives 800‑MeV (≈0.84c) protons onto tungsten spallation targets, producing neutrons from thermal energies up to several hundred MeV. These beams enable a broad portfolio of national‑impact science and technology.

LANSCE's high‑energy proton beams power five user facilities:

• Lujan Center
• Weapons Neutron Research (WNR)
• Ultra‑Cold Neutron (UCN) Source
• Proton Radiography (pRad)
• Isotope Production Facility (IPF)

Within P‑2, the Spallation Physics Team designs and optimizes spallation targets, radiological shielding, neutron transport, and background reduction across experimental areas, while supporting daily facility operations.

As a postdoctoral researcher, you will help optimize next‑generation spallation targets delivering thermal/epithermal neutrons to Lujan Center instruments and versatile fast‑neutron spectra to multiple WNR flight paths. A major focus is modernizing our end‑to‑end simulation workflow, from as‑built geometry capture to high‑fidelity transport:

• Run end‑to‑end Monte Carlo (MCNP) simulation campaigns using as‑built geometry to optimize spallation‑target and beamline setups.
• Integrate laser‑tracker/LiDAR scans into CAD; generate analysis‑ready meshes and use MCNP Unstructured Mesh / hybrid geometry for high‑fidelity neutron‑transport simulations.
• Operate simulations on the local HPC (~6,000 cores + GPUs).
• Validate MCNP simulations against measurements; perform sensitivity analyses; collaborate with teammates and MCNP developers; document and present results.
• Develop Python‑based, reproducible MCNP simulation pipelines for pre/post‑processing, meshing, input generation, and results reduction.
• Apply ML/AI (surrogate models, Bayesian optimization, active learning) and computer vision to accelerate MCNP simulation‑driven optimization and prepare scan/mesh inputs.

• Develop and maintain CAD and mesh‑based models informed by 3D scans (LiDAR, laser tracker).
• Design and simulate spallation targets, moderators, reflectors, and shielding using MCNP (or comparable transport codes).
• Analyze experimental and simulation data; validate models against measurements.
• Automate workflows with Python; maintain reproducible analyses using Git.
• Document methods and results; present to internal reviews and external users.
• Support safe, reliable operations in LANSCE experimental areas

• Strong desire to learn and grow technically; proactive, solution‑oriented mindset.
• Effective communication and teamwork in a multidisciplinary environment.
• Demonstrated experience with MCNP or other particle‑transport codes (e.g., Geant4, FLUKA, PHITS).
• Data‑analysis experience (experimental and/or simulation).
• Excellent writing and presentation skills.
• Proficiency in Python (core to our workflows).

• Comfortable with UNIX/Linux environments.
• Contributions to both experimental (hands‑on) and simulation work.
• Strong scripting:
  Bash and Python; familiarity with Fortran, C/C++, or Perl is a plus.
• Skilled with CAD (e.g., Solid Works, Inventor, Space Claim).
• Practical experience with 3D scanning/meshing/CAD and 3D printing.
• Experience using LiDAR or laser trackers.
• Uses Git for version control and team workflows.
• Demonstrated creative, unconventional problem‑solving.
• Robotics exposure (not required).
• ML/AI in scientific workflows: experience with one or more of
  • surrogate/emulator modeling and Bayesian optimization for design;
  • computer vision for point‑cloud/mesh/scan processing;
  • uncertainty quantification or active learning;
  • common libraries/tools (e.g., scikit‑learn, PyTorch or Tensor Flow, OpenCV).

We value learning and problem‑solving over a perfect résumé match. If our mission resonates with you, please apply—even if you don’t meet every requirement. Robotics experience and deeper ML/AI expertise are strong pluses, but they’re not gatekeepers; curiosity, initiative, and a willingness to learn matter most, and we encourage candidates to…