Senior Applied Research Scientist – GPU Native Numerical Algorithms

NVIDIA

. Invent and reformulate numerical algorithms co-designed for modern NVIDIA GPU architectures, including implicit and explicit engineering simulation .

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing numerical algorithms for GPU architectures, with a strong foundation in computational mechanics and applied mathematics. Proficient in collaborating across research and engineering teams to advance GPU-native numerical methods and their applications in various engineering domains.

Highest-signal resume keywords

PhD In Computational MechanicsNumerical Software Development In C++ And PythonExperience With CUDA And GPU Code OptimizationResearch Experience With PDE Discretization And Nonlinear SolversFamiliarity With Industrial Simulation Tools Like Ansys And Abaqus

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Hard Skills

Numerical Algorithms DevelopmentFinite Element MethodsSparse Linear AlgebraHigh-Performance ComputingMatrix-Free AlgorithmsPreconditioning TechniquesPerformance AnalysisImplicit Structural DynamicsNonlinear MechanicsMultiphysics Simulation

Soft Skills

Technical CommunicationCollaboration Across Teams

Tools & Technologies

CUDA-XNVIDIA WarpPETScTrilinosOpenFOAMCuSPARSECuSOLVERMPINCCLMathWorks

Industry Keywords

Computational ScienceEngineering SimulationEDACAECADSemiconductor ProcessThermal-Fluid SystemsElectromagneticsContact MechanicsDistributed Solvers

Tech Stack

Tools & technologies

PythonC++

About the role

Key responsibilities & impact

Invent and reformulate numerical algorithms co-designed for modern NVIDIA GPU architectures, including implicit and explicit engineering simulation
Develop linear and nonlinear solver approaches, including Newton-Krylov, multigrid and AMG, domain decomposition, matrix-free algorithms, mixed precision, sparse iterative and direct methods, and preconditioning
Investigate reformulation or replacement of CPU-oriented numerical methods for GPU architectures
Evaluate algorithms on mechanics, contact, thermal-fluid systems, electromagnetics, semiconductor process and device simulation, EDA, multiphysics, and related CAE workloads
Collaborate with CUDA-X, Warp, solver engineering, NVIDIA Research, universities, and industry partners to move research prototypes into NVIDIA software capabilities
Shape the applied research roadmap for GPU-native numerical methods and their evolution toward AI-native computational engineering

Requirements

What you’ll need

PhD or equivalent experience in computational mechanics, applied mathematics, scientific computing, computer science, aerospace, mechanical, civil engineering, or a related technical field
5+ years of relevant work/research experience
Research or engineering experience with PDE discretization, finite element, finite volume, discontinuous Galerkin methods, nonlinear solvers, sparse linear algebra, preconditioning, or high-performance computing
Experience writing numerical software in C++ and Python, plus experience developing or optimizing CUDA or GPU code
Experience using profiling, benchmarking, numerical validation, or performance analysis to improve algorithms on GPU or multi-GPU systems
Ability to communicate technical tradeoffs clearly and collaborate across research, engineering, product, and partner teams
Experience with implicit structural dynamics, nonlinear mechanics, contact, CFD, electromagnetics, multiphysics, semiconductor simulation, EDA, CAE, or CAD-connected engineering workflows
Contributions to or practical experience with PETSc, Trilinos, MFEM, libCEED, OpenFOAM, NVIDIA Warp, CUDA-X, cuSPARSE, cuSOLVER, or related computational science frameworks
Experience with industrial simulation, EDA, semiconductor, CAE, or CAD ecosystems, including Ansys, Abaqus, LS-DYNA, Siemens Simcenter, Dassault SIMULIA, Altair, Cadence, Synopsys, COMSOL, MathWorks, or comparable internal solver and design platforms
Experience with distributed solvers using MPI, NCCL, asynchronous methods, or performance analysis on GPU clusters
Publications, patents, open-source work, or deployed software in computational science venues or communities such as SC, SIAM CSE, SIAM SISC, CMAME, IJNME, JCP, AIAA, USNCCM, WCCM, or related areas