Postdoctoral Researcher - Operator Networks for Hydrodynamics & Inertial Confinement Fusion

About the role

The Applied Math and Plasma Physics Group (T-5) in the Theoretical Division at Los Alamos National Laboratory seeks 1-2 outstanding postdoctoral researchers to advance physics-consistent operator networks for hydrodynamics and inertial confinement fusion (ICF) applications. The work will span theory, algorithms, and scalable implementations on cutting-edge HPC systems.

Responsibilities

• Design, analyze, and implement operator neural networks (e.g., DeepONets, Fourier/Neural Operators, graph/mesh operators) that respect conservation laws, symmetries, and stability constraints relevant to compressible hydro, radiation-hydrodynamics, and MHD.
• Develop mathematical foundations for operator learning: approximation/error bounds, well-posedness, stability, and generalization for PDE-to-PDE maps; contribute proofs and rigorous analyses.
• Integrate physics into learning (e.g., variational/energy principles, constrained training, differentiable solvers, PINO/PINN-style residuals) and build robust surrogates for stiff, multi-scale flows.
• Run large-scale computational experiments on CPU/GPU clusters; compare against high-fidelity solvers and community benchmarks.
• Collaborate across math, CS, and plasma physics, publish in leading journals, present at conferences, and engage with external research partners.

Requirements

• Minimum Job Requirements:
  • Education: Ph.D. (earned within the last 5 years) in Applied Mathematics, Mathematics, or a closely related field (e.g., CS, Statistics, Physics, Engineering).
    • Mathematical depth: Demonstrated strength in functional analysis/operator theory, numerical PDEs, approximation theory, and/or stochastic processes; ability to craft rigorous arguments (stability, error estimates, convergence).
  • Scientific ML: Research experience in neural operators / scientific machine learning (operator learning, PINNs/PINN, PDE-constrained learning, model reduction) with a strong publication record.
    • Programming: Proficiency in Python and one or more of PyTorch/JAX/TensorFlow; working knowledge of scientific computing (vectorization, profiling, testing).
  • Communication: Clear written and oral communication, as shown through publications and the cover letter.
• Desired Qualifications:
  • Experience with hydrodynamics/ICF/HEDP physics (e.g., shocks, turbulence, EOS, radiation transport, MHD) or with high-order/structure-preserving numerical methods.
    • HPC skills (MPI, OpenMP, CUDA, GPU acceleration), containers, CI, and collaborative software development.
  • Contributions to open-source ML/scientific software; code reviews; reproducible workflows.
    • Background in UQ/data assimilation/Bayesian inference, multi-fidelity or active learning for PDE models.
  • Evidence of competitive excellence (e.g., research awards, notable competitions) and effective teamwork.

Qualifications

• Education/Experience: A Ph.D. in Applied Mathematics, Mathematics, Computer Science, Statistics, Physics, or a closely related field, earned within the last 5 years.

Work Location

On-site in Los Alamos, NM.