Strengthen the United States’ security by advancing fundamental R&D at the intersection of reduced-order modeling, foundation models for the computational sciences, and statistical/machine learning methods
Requirements
- Demonstrated expertise in spatial discretization methods (finite element, finite volume, and finite difference methods)
- Strong background in numerical time integration techniques
- Experience building and evaluating modern ML models using PyTorch, TensorFlow, and/or JAX
- Significant software development experience with the Python scientific software stack
- 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
Responsibilities
- 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
Other
- Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA)
- 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
- Must pass a post-offer, pre-employment drug test
- Must be aware of and comply with restrictions on the use and/or possession of mobile devices in Limited Areas
