Postdoc: AI-Driven Reduced-Order Modeling for Physics

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

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