Computational Scientist

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