Physics-Informed Machine Learning - Postdoctoral Researcher

LLNL • Livermore, CA

5d • Hybrid

About The Position

Lawrence Livermore National Laboratory is hiring a 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 create physics-informed models that improve the accuracy, robustness, and computational efficiency of scientific simulations. Research may involve 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. It offers a hybrid schedule, with the flexibility to work from home one or more days per week. This is a temporary, mentored postdoctoral appointment 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.

The position requires the ability to obtain and maintain a U.S. Department of Energy Q-level security clearance, which requires U.S. citizenship. The annual pay listed is $123,048. Salary placement may depend on factors including competencies, education, qualifications, experience, skills, location, performance, and organizational needs.

Requirements

• Ph.D. in computational mechanics, mechanical engineering, applied mathematics, computational science, physics, materials science, or a related field.
• Must have received a Ph.D. or equivalent doctorate within five years of the anticipated start date.
• Ability to secure and maintain a U.S. DOE Q-level security clearance; U.S. citizenship is required.
• 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.

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 but complementary research interests and interact with scientists within and outside the Laboratory.
• Collaborate with a multidisciplinary team to accomplish research goals.
• Publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, 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