Physics-Informed Machine Learning - Postdoctoral Researcher

LLNL • Livermore, CA

4d • Hybrid

About The Position

Lawrence Livermore National Laboratory is seeking a Physics-Informed Machine Learning Postdoctoral Researcher to develop and apply advanced computational methods for modeling complex physical systems. The researcher will combine machine learning, computational mechanics, and numerical methods to develop physics-informed models that improve the accuracy, robustness, and computational efficiency of scientific simulations. Potential research includes 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. The position is in the Atmospheric, Earth and Energy Division within the Physical and Life Sciences Directorate.

The role offers a hybrid schedule, with in-person and virtual work and the flexibility to work from home one or more days per week. The postdoctoral appointment is a temporary, mentored training position for researchers in the early stages of their postdoctoral careers. It is initially for two years, with the possibility of extension to a maximum of three years. Applicants must have received their Ph.D. or equivalent doctorate within five years of the anticipated start date.

Requirements

• Ability to secure and maintain a U.S. DOE Q-level security clearance; U.S. citizenship is required.
• Ph.D. 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 high-performance computing environments.
• Ability to develop independent research projects, including publication of peer-reviewed literature, and to communicate technical information effectively.
• Ability to work collaboratively in a multidisciplinary team environment.
• Proficient verbal and written communication skills to collaborate effectively and present and explain technical information.
• Must have received a Ph.D. or equivalent doctorate within five years of the anticipated start date.

Nice To Haves

• Experience with physics-informed neural networks, neural operators, differentiable programming, or hybrid physics-based and data-driven models.
• Experience with deep learning, transfer learning, generative models, surrogate modeling, or reduced-order modeling.
• Experience with finite-element methods, computational fluid dynamics, solid mechanics, multiphysics simulation, or related computational frameworks.
• Experience with Python and machine-learning frameworks such as PyTorch, JAX, or TensorFlow.

Responsibilities

• 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, complementary research interests and interact with 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.

Benefits

• Flexible benefits package.
• 401(k).
• Relocation assistance.
• Education reimbursement program.
• Flexible schedules, depending on project needs.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree