PhD in computational physics, applied mathematics, computational engineering, or related field
Expertise in numerical PDE methods: FEM, FVM, or BEM
Strong C++ and CUDA programming skills
Experience with multi-node HPC: MPI and domain decomposition
Deep knowledge of sparse linear algebra methods
Experience with neural operators like FNO and DeepONet
Understanding of AI methodologies for simulation and model evaluation
Responsibilities
Develop and scale MPI+CUDA PDE solvers for complex 3D IC geometry problems
Tune and enhance AMG preconditioners and Krylov solvers for performance
Build and train neural operators as high-fidelity surrogates for PDE solvers
Design simulation pipelines for generating training data for neural models
Validate solutions against analytical results and benchmarks
Benefits
Work in a dynamic environment backed by top investors and industry leaders
Collaborate with a team of experienced professionals, including former Stanford faculty
Engage in cutting-edge projects at the intersection of AI and physical sciences
Opportunity to contribute to high-stakes, impactful innovations in hardware design
Access to advanced resources for research and development in computational physics
Full Job Description
What You'll Work On
Develop and scale MPI+CUDA PDE solvers for electrostatics, charge transport, and electromagnetic field problems on complex 3D IC geometries across multi-node GPU clusters
Tune and extend AMG preconditioners, Krylov solvers, and mesh pipelines for performance and correctness at scale
Build and train neural operators (FNO, DeepONet, GNO, and variants) as high-fidelity surrogates for PDE-based field solvers
Design simulation pipelines that generate training data for neural operator models - including sampling strategies, mesh handling, and physical consistency checks
Validate everything: analytical solutions, published benchmarks, and cross-validation between field solvers and learned surrogates
Required
PhD in computational physics, applied mathematics, computational engineering, or a closely related field
Deep expertise in numerical PDE methods: FEM, FVM, or BEM - weak formulations, quadrature, convergence, error analysis
Strong C++ and CUDA - writing and optimizing kernels, memory hierarchy, multi-GPU programming
Sparse linear algebra at depth: Krylov methods, algebraic multigrid, preconditioning strategies
Hands-on experience with neural operators (FNO, DeepONet, or equivalent) - training, architecture design, and evaluation on PDE datasets
Solid understanding of AI for Science methodology: how to design datasets from simulations, handle out-of-distribution generalization, and ensure physical consistency of learned models
Strongly Preferred
Experience with HYPRE, PETSc, and Trilinos
Familiarity with multi-node GPU clusters: NCCL, CUDA-aware MPI, NVLink topologies
Published work in neural operators, physics-informed ML, or scientific HPC
IC design domain knowledge: device physics, semiconductor materials, layout data formats
