Postdoc: Physics-Informed Neural Operators for PDEs

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 65,000 - 90,000; Full time

Job summary

Los Alamos National Laboratory invites applications for 1-2 postdoctoral researchers to advance physics-consistent operator networks for hydrodynamics and inertial confinement fusion (ICF). The role spans theory, algorithms, and scalable HPC implementations on cutting-edge hardware.

Responsibilities include designing neural operators, developing mathematical foundations, integrating physics into learning, and running large-scale experiments.

Qualifications

• PhD earned within the last 5 years in a related field (Applied Mathematics, Mathematics, CS, Statistics, Physics, or Engineering).
• Strong background in functional analysis and numerical PDEs; ability to craft rigorous arguments and proofs.
• Experience in neural operators / scientific ML with publications.
• Proficient in Python and ML frameworks (PyTorch/JAX/TensorFlow).
• Excellent written and oral communication demonstrated through publications and letters.

Responsibilities

• Design, analyze, and implement operator neural networks for hydrodynamics and ICF.
• Develop mathematical foundations for operator learning and provide rigorous analyses.
• Integrate physics into learning via variational principles and differentiable solvers.
• Run large-scale computational experiments on CPU/GPU clusters and compare with high-fidelity solvers.
• Collaborate across math, CS, and plasma physics; publish and present findings.

Skills

Mathematical depth

Scientific ML

Scientific computing

Communication skills

Education

PhD in a relevant field

Tools

Python

PyTorch

JAX

TensorFlow

Job description

Los Alamos National Laboratory invites applications for 1-2 postdoctoral researchers to advance physics-consistent operator networks for hydrodynamics and inertial confinement fusion (ICF). The role spans theory, algorithms, and scalable HPC implementations on cutting-edge hardware.

Responsibilities include designing neural operators, developing mathematical foundations, integrating physics into learning, and running large-scale experiments.