Postdoctoral Researcher — Operator Networks Hydrodynamics & Inertial Confinement Fusion

What You Will Do

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.

As a postdoc you will:

• 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.

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/PINOs, PDE-constrained learning, model reduction) with a strong publication record.
• Programming: Proficiency in Python and one or more of PyTorch/JAX/Tensor Flow; 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.

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

On-site in Los Alamos, NM.

For questions, contact Marc Klasky (mklasky). Include in your application:

• CV
• Cover letter describing research interests, relevant math/ML background, and career goals
• Contact information for two references
• Please submit materials via the LANL Jobs website.

Work Location:

The work location for this position has the potential for remote work. Remote work is defined as working offsite that is greater than 2 hours of the Laboratory location. Reporting onsite may be periodically necessary. All work locations are at the discretion of management.

Note to Applicants:
Due to federal restrictions contained in the current National Defense Authorization Act, citizens of the People's Republic of China—including the special administrative regions of Hong Kong and Macau—as well as citizens of the Islamic Republic of Iran, the Democratic People’s Republic of Korea (North Korea), and the Russian Federation, who are not Lawful Permanent Residents are prohibited from accessing facilities that support the mission, functions, and operations of national security laboratories and nuclear weapons production facilities, which includes Los Alamos National…