ML-Driven Plasma Physics Postdoc

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 87,000 - 107,000; Full time

Benefits offered by this job

Medical insurance (PPO or HDHP) with a

Dental and vision coverage

Life and disability insurance

Relocation packages

Job summary

Los Alamos National Laboratory seeks a postdoctoral researcher to develop machine learning–enabled complexity reduction methods for multiscale plasma physics simulations. The work involves reduced-order modeling, surrogate modeling, uncertainty quantification, and scalable algorithms, with collaboration across LANL divisions and international teams.

The candidate will work with a multi-disciplinary group of statisticians, mathematicians, and physicists, contributing to cutting-edge research and

Qualifications

• Ph.D. completed within the past five years or soon to be completed.
• Experience with Machine Learning for science applications.
• Experience programming in Fortran, C++, Python.
• Experience with numerical methods.
• Demonstrated research ability in computational plasma physics and/or machine learning.
• Excellent communication skills (oral and written).

Responsibilities

• Develop machine learning-enabled complexity reduction methods for multiscale plasma physics simulations.
• Collaborate with statisticians, applied mathematicians, and physicists within T-5 and with LANL groups.
• Engage across teams in the U.S. and worldwide to advance scientific frontiers.

Skills

Machine Learning for science apps

Fortran

C++

Python

Numerical methods

Communication skills

Education

Ph.D. in computational plasma physics / applied mathematics / computational science

Job description

Los Alamos National Laboratory seeks a postdoctoral researcher to develop machine learning–enabled complexity reduction methods for multiscale plasma physics simulations. The work involves reduced-order modeling, surrogate modeling, uncertainty quantification, and scalable algorithms, with collaboration across LANL divisions and international teams.

The candidate will work with a multi-disciplinary group of statisticians, mathematicians, and physicists, contributing to cutting-edge research and