Hybrid Modeling and Machine Learning - Postdoctoral Researcher

Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory’s mission.

Pay Range

$138,480 Annually

Job Description

We are looking for a postdoctoral researcher to advance fundamental R&D at the intersection of reduced-order modeling, foundation models for the computational sciences, and statistical/machine learning methods. You will help develop scalable models and methods for materials science, fusion, and additive manufacturing, and lead decision-making process, such as design optimization and uncertainty quantification and rigorous validation of deep learning integrated with simulation. Furthermore, you will develop methods to improve interpretability and trustworthiness of these models. This position will be in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate.

Essential Duties

• Research & prototype hybrid reduced order model/machine learning methods (DD-FEM, LaSDI, gappy Autoencoder, proper orthogonal decomposition, and neural operators) tightly coupled to governing equations.
• Design discretizations & integrators: derive/implement stable and accurate spatial discretizations (e.g., finite element, finite volume, finite difference methods) and time integrators (e.g., explicit/implicit Runge–Kutta, multistep/BDF).
• Guarantee physics & reliability: enforce conservation/stability, perform error and sensitivity analysis, and lead UQ, and explainability for hybrid models.
• Integrate with HPC codes and experimental/simulation workflows. 
• Publish results, contribute proposals, and collaborate across disciplines.
• Perform other duties as assigned.

Qualifications

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA).  See Additional Information section below for details.
• Ph.D. in Computational Science, Applied Mathematics, Engineering, Statistics, or a related field.
• Demonstrated ability and desire to obtain substantial domain knowledge in fields of application to enable effective communication with subject matter experts, and to identify novel, impactful applications of machine learning.
• Demonstrated expertise in spatial discretization methods (finite element, finite volume, and finite difference methods).
• Strong background in numerical time integration techniques.  Demonstrated experience with numerical analysis, including stability and convergence.
• Experience building and evaluating modern ML models using PyTorch, TensorFlow, and/or JAX.
• Significant software development experience with the Python scientific software stack; demonstrated experience following modern software engineering practices (e.g. testing, version control, reproducibility).
• Significant experience building and evaluating reduced order models using Proper Orthogonal Decomposition, Dynamic mode decomposition, and/or hyper-reduction.
• Significant experience using the libROM software library or equivalent.
• Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software, in relevant venues (NeurIPS, ICML, JCP, CMAME, Science, IJNME, SISC, Nature etc.).

Desired Qualifications

• Experience with high-performance computing, GPU programming, parallel programming, and/or related methods including running numerical simulations of complex workflows.
• Demonstrated technical leadership in fields related to computational science and machine learning, such as mentorship or managing teams.
• Experience or interest in scientific applications, such as, material science, climate science, fusion, earthquake, and additive manufacturing.

Additional Information

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

• Included in 2025 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Re