Postdoctoral - Reduced Order Modeling - Research Staff Member

Lawrence Livermore National Laboratory; Livermore (CA); On-site; USD 129,000 - 158,000; Full time

Benefits offered by this job

Relocation Assistance

Education Reimbursement

Flexible schedules

401(k)

Job summary

Lawrence Livermore National Laboratory in Livermore, CA, invites applications for a Postdoctoral Research Staff Member in the Center for Applied Scientific Computing (CASC). The role focuses on computational science, scientific ML, reduced-order modeling, and data-driven methods to build fast, trustworthy surrogate models that couple physics simulations with experimental data and AI-assisted workflows.

You will collaborate with experimentalists and computational scientists to develop

Qualifications

• PhD in computational science, applied mathematics, computational engineering, mechanical, chemical engineering, computer science, or related field.
• Experience developing or applying computational models to physical or engineering systems.
• Proficiency in Python, C++, C, FORTRAN, or similar computing languages.

Responsibilities

• Conduct research in scientific machine learning, reduced-order modeling, surrogate modeling, system identification, uncertainty quantification, and related areas.
• Develop data-efficient methods combining high-fidelity simulations with sparse experimental data.
• Collaborate across multidisciplinary teams to advance surrogate models and AI-assisted workflows.
• Publish research results in peer-reviewed venues and present at conferences.

Skills

scientific machine learning

reduced-order modeling

surrogate modeling

system identification

uncertainty quantification

optimization

numerical methods

data-driven modeling

Education

PhD in a relevant field

Tools

Python

C++

FORTRAN

scientific computing

Job description

Company Description

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.

Job Description

The Center for Applied Scientific Computing (CASC) within the Computing Directorate, is seeking a Postdoctoral Research Staff Member with a strong background in computational science, scientific machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that integrate physics-based simulations with experimental data and enable artificial intelligence (AI)-agent-assisted scientific workflows.

This position will contribute to multidisciplinary research connecting physical experiments, high-fidelity computational models, surrogate and reduced-order models, and AI agents within automated Design-Build-Test-Compute workflows. Research opportunities include developing methods to reconcile discrepancies between computational models and physical experiments, constructing surrogate models from sparse experimental data, identifying low-dimensional representations of high-dimensional parameter spaces, developing uncertainty-aware and adaptive models, and integrating computational models with AI agents for scientific decision support. Applications will include electrochemical systems and advanced manufacturing, with opportunities to develop broadly applicable methods and software for computational science.

You will

• Conduct research and development in one or more of the following areas: scientific machine learning, reduced-order modeling, surrogate modeling, system identification, equation discovery, uncertainty quantification, active learning, optimization, computational mechanics, and data-driven modeling of physical systems.
• Develop data-efficient computational methods for constructing predictive models from combinations of high-fidelity simulation data and sparse, noisy, or time-dependent experimental observations.
• Develop approaches for reconciling computational models with physical experiments, including parameter calibration, model-discrepancy correction, equation discovery, and hybrid physics/data-driven modeling.
• Develop methods for identifying low-dimensional parameter spaces, latent representations, or active subspaces that enable efficient surrogate construction and experimental exploration.
• Develop uncertainty-aware surrogate models and methods for assessing model validity, detecting out-of-distribution operating conditions, and determining when model predictions can be reliably used for scientific decision support.
• Investigate active-learning and adaptive experimental-design strategies in which computational models and AI agents identify informative operating conditions or experiments for improving surrogate models.
• Develop and integrate surrogate and reduced-order models with AI-agent-compatible software interfaces to enable automated model execution, model updating, optimization, and decision support.
• Apply developed methods to multidisciplinary applications involving physical experiments and computational simulations, including electrochemical systems and advanced manufacturing.
• Design and perform numerical experiments to evaluate model accuracy, computational performance, data efficiency, uncertainty, robustness, and generalization.
• Contribute to the development and utilization of LLNL computational tools for reduced-order modeling and scientific machine learning.
• Contribute to and actively participate in the conception, design, and execution of research addressing defined scientific and engineering problems.
• Pursue independent but complementary research interests and interact with a broad spectrum of scientists and engineers internally and externally to the Laboratory.
• Collaborate with experimentalists, computational scientists, applied mathematicians, software developers, and AI researchers in a multidisciplinary team environment.
• Publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, and technical meetings.
• Perform other duties as assigned.

Qualifications

• PhD in computational science, applied mathematics, computational engineering, mechanical engineering, chemical engineering, computer science, or a related field.
• Experience in one or more of the following areas: scientific machine learning, reduced-order modeling, surrogate modeling, system identification, uncertainty quantification, optimization, numerical methods, or data-driven modeling of physical systems.
• Experience developing or applying computational models to physical or engineering systems.
• Proficiency in Python, C++, C, FORTRAN, or similar scientific computing languages.
• Demonstrated ability to develop and evaluate computational methods using simulation and/or experimental data.
• Demonstrated ability to conduct independent research, as evidenced by publicationsin peer-reviewed scientific or technical literature.
• Proficient verbal and written communication skills necessary to collaborate effectively in a multidisciplinary team environment and present and explain technical information.
• Demonstrated initiative, creativity, and interpersonal skills as well as the ability to work effectively with researchers from computational, experimental, and software-development backgrounds.

Qualifications We Desire

• Experience with reduced-order modeling, surrogate modeling, or scientific machine learning for physical or engineering systems.
• Experience with system identification, equation discovery, or hybrid physics/data-driven modeling for improving or augmenting physics-based computational models.
• Experience with uncertainty quantification, active learning, sensitivity analysis, or adaptive experimental design, particularly for data-scarce physical systems.
• Experience integrating simulation and experimental data for model calibration, validation, prediction, or decision support.
• Experience with high-performance computing and/or modern machine-learning frameworks, such as PyTorch or JAX.
• Familiarity with AI agents, tool-enabled AI workflows, Model Context Protocol interfaces, or automated scientific workflows.

Salary Range

$143,328 Annually

Additional Information

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, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

• Included in 2026Best Places to Work by Glassdoor!
• FlexibleBenefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visithttps://www.llnl.gov/inclusion/our-values

Security Clearance

None required.However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check.

National Defense Authorization Act (NDAA)

The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities. The restrictions of NDAA Section 3112 apply to this position. To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

Equal Employment Opportunity

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.

CaliforniaPrivacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.