Senior Applied Research Scientist – Computational Geometry, Meshing
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Job Description
Applied Research Scientist advancing computational geometry, meshing, and AI-native algorithms for NVIDIA GPU platforms. Improving CAD-to-simulation workflows across engineering, semiconductor, and scientific-computing applications.
Responsibilities:
- Build algorithms for computational geometry, computer-aided engineering and design interoperability, mesh generation, mesh adaptation, spatial data structures, and curved discretization
- Investigate differentiable geometry, AI-native geometry processing, learning-based meshing and discretization, and design-to-simulation workflows
- Develop methods for inverse design, simulation-ready digital twins, and autonomous engineering workflows
- Explore solver- and hardware-aware geometry and discretization methods optimizing mesh quality, numerical accuracy, robustness, and simulation efficiency
- Define benchmarks for mesh quality, geometry conversion, discretization accuracy, robustness, downstream solver impact, and end-to-end simulation performance
- Collaborate with teams across Omniverse, OpenUSD, Warp, solver engineering, NVIDIA Research, universities, and industrial partners in CAE, EDA, chip manufacturing, electronics, and digital twin workflows
Requirements:
- PhD or equivalent experience in computer science, computational geometry, scientific computing, graphics, applied mathematics, computational mechanics, engineering, or a related field
- 5+ years of experience
- 5+ years of proven experience working in computational engineering, with a background in computational geometry, geometry processing, mesh generation, adaptive discretization, CAD and CAE algorithms, or simulation-ready representations
- C++ and Python skills, with experience building sophisticated geometry algorithms
- Understanding of boundary representations, topology, mesh quality, discretization error, numerical robustness, and solver requirements
- Experience with CAD kernels or formats such as Parasolid, ACIS, Open Cascade, CATIA, NX, Creo, SOLIDWORKS, STEP, IGES, B-Rep, NURBS, or spline-based representations
- Experience with tetrahedral, hexahedral, polyhedral, anisotropic, adaptive, boundary-layer, curved, or high-order mesh generation
- Work in isogeometric analysis, remeshing, meshless methods, topology optimization, shape optimization, differentiable geometry, or AI-native mesh generation
- Experience with geometry repair, feature or simulation-intent recognition, parameterization, persistent correspondence, learning-based geometry representations, GPU spatial algorithms, or solver-aware adaptation
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