Computational Scientist
Develops and scales MPI+CUDA PDE solvers and neural operators for electromagnetic simulations on GPU clusters for IC design. Requires PhD in computational physics/math, expertise in numerical methods, C++/CUDA, HPC, and neural operators for physics problems.
About the job
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
- Multi-node HPC: MPI, domain decomposition, collective communication, strong/weak scaling
- 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
Skills
C++, CUDA, Mpi, Fem, Fvm, Bem, Krylov Methods, Algebraic Multigrid, Neural Operators, Fno, Deeponet, Hypre, Petsc, Trilinos, Nccl
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