Physics-Informed Machine Learning - Postdoctoral Researcher

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.  You will • 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 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 in a team environment and present and explain technical information. Qualifications We Desire • 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. Pay Range $123,048 Annually This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. This is a 2-year Postdoctoral appointment with the possibility of extension to a maximum of three years. The postdoctoral appointment is a temporary, mentored training position intended for individuals in the early stages of their postdoctoral research careers. Consistent with the structure of the program, applicants must have received their Ph.D. (or equivalent doctorate) within five years of the anticipated start date. All your information will be kept confidential according to EEO guidelines. Position Information This is a Postdoctoral appointment with the possibility of extension to a maximum of three years. The postdoctoral appointment is a temporary, mentored training position intended for individuals in the early stages of their postdoctoral research careers. Consistent with the structure of the program, applicants must have received their Ph.D. (or equivalent doctorate) within five years of the anticipated start date. Why Lawrence Livermore National Laboratory? Included in 2026 Best Places to Work by Glassdoor! Flexible  Benefits Package 401(k) Relocation Assistance Education Reimbursement Program Flexible schedules (*depending on project needs) Our values - visit  https://www.llnl.gov/inclusion/our-values Security Clearance This position requires a Department of Energy (DOE) Q-le