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Los Alamos National Laboratory · 21 hours ago

Postdoctoral Researcher - Operator Networks for Hydrodynamics & Inertial Confinement Fusion

Los Alamos, NM

Full-time

Hybrid

New Grad, Entry Level

Los Alamos National Laboratory is a multidisciplinary research institution engaged in strategic science on behalf of national security. They are seeking outstanding postdoctoral researchers to advance physics-consistent operator networks for hydrodynamics and inertial confinement fusion applications, involving theory, algorithms, and scalable implementations on HPC systems.

Security

Growth Opportunities

No H1B

U.S. Citizen Only

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

Qualification

Operator neural networksScientific machine learningPython programmingFunctional analysisNumerical PDEsHPC skillsClear communicationTeamwork

Required

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/PINOs, 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

Preferred

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