Physics-Informed Machine Learning - Postdoctoral Researcher

Mid-Senior Level | Full-time

Physical Life Sciences | Livermore, CA | 11/02/2026

Reference #: REF
9094L

Job Code: PDS.
1 Post-Dr Research Staff 1

Organization: Physical and Life Sciences

Position Type: Post Doctoral

Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)

Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)

Medical Exam: Not applicable

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

We have an opening for a Physics-Informed Machine Learning Postdoctoral Researcher to develop and apply advanced computational methods for modeling complex physical systems.

You will combine expertise in machine learning, computational mechanics, and numerical methods to develop physics-informed models that improve the accuracy, robustness, and computational efficiency of scientific simulations. Research may include developing machine-learning models constrained by physical laws, learning constitutive relationships from experimental or simulation data, developing surrogate and reduced-order models, and integrating data-driven approaches into computational mechanics workflows. This position is in the Atmospheric, Earth and Energy Division within the Physical and Life Sciences Directorate.

This position offers a hybrid schedule, blending in-person and virtual presence. You will have the flexibility to work from home one or more days per week.

• Develop physics-informed machine-learning methods for computational mechanics and scientific simulation.
• Integrate governing equations, conservation laws, boundary conditions, and material constraints into machine-learning models.
• Develop, train, validate, and evaluate neural networks, surrogate models, reduced-order models, and other scientific machine-learning methods.
• Analyze computational and experimental data to identify physical relationships, model parameters, and constitutive behavior.
• Perform computational mechanics simulations using finite-element, finite-volume, or related numerical methods.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
• Collaborate with others in a multidisciplinary team environment to accomplish research goals.
• Publish research results in peer-reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
• Perform other duties as assigned

• Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. citizenship.
• PhD in computational mechanics, mechanical engineering, applied mathematics, computational science, physics, materials science, or a related field.
• Demonstrated experience with physics-informed machine learning, scientific machine learning, or related data-driven modeling approaches.
• Knowledge of computational solid and/or fluid mechanics, numerical methods, and contact mechanics for Lagrangian finite element methods.
• Experience with computational simulations and analysis of scientific or engineering data. Experience with…