Senior Applied Research Scientist – Computational Geometry, Meshing

NVIDIA

. Build algorithms for computational geometry, computer-aided engineering and design interoperability, mesh generation, mesh adaptation, spatial data structures, and curved discretization .

Posted 10/31/2026full-timeRemote • GermanySenior💰 €121,275 - €255,970 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in computational geometry, algorithm development for mesh generation, and simulation-ready digital twins. Proficient in C++ and Python, with a strong background in CAD and CAE algorithms, ensuring high-quality mesh and numerical accuracy.

Highest-signal resume keywords

PhD In Computer Science Or Related Field5+ Years In Computational EngineeringC++ And Python ProgrammingExperience With CAD Kernels And FormatsExpertise In Mesh Generation Techniques

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Computational GeometryMesh GenerationAdaptive DiscretizationGeometry ProcessingInverse Design MethodsNumerical RobustnessSolver RequirementsDifferentiable GeometryAI-Native Geometry ProcessingSimulation-Ready Representations

Tools & Technologies

ParasolidACISOpen CascadeCATIANXCreoSOLIDWORKSSTEPIGESB-Rep

Industry Keywords

Computer-Aided EngineeringDigital Twin WorkflowsElectronicsChip ManufacturingEnd-To-End Simulation Performance

Tech Stack

Tools & technologies

PythonC++

About the role

Key responsibilities & impact

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

What you’ll need

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