GPU-Driven Differentiable Simulation Intern

NVIDIA; Santa Clara (CA); On-site; USD 52,000 - 129,000; Part time

Benefits offered by this job

Intern benefits

Job summary

NVIDIA invites a PhD candidate to join a research internship focused on scalable differentiable algorithms. You will connect simulations with experimental data for calibration and data assimilation using AI/ML methods, evaluating on CFD, mechanics, thermal systems, and multiphysics workloads.

Collaboration across mathematics, code, hardware, and engineering applications is expected. If you enjoy cross-disciplinary work and advancing numerical methods on GPUs, this internship offers meaningful,

Qualifications

• Pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or equivalent program area.
• Strong foundations in PDE-related algorithms, numerical linear algebra, iterative solvers, optimization, or scientific computing.
• Research experience with adjoint methods, automatic differentiation, inverse problems, data assimilation, gradient-aware simulation approaches, or related areas.
• Experience writing numerical software in Python and C++, with familiarity with parallel or GPU computing.
• Ability to design computational experiments, evaluate numerical and performance tradeoffs, and communicate results clearly.

Responsibilities

• Prototype GPU-native solvers with adjoint and automatic differentiation techniques for engineering simulations.
• Integrate AI-based methods with simulation algorithms for parameter calibration, inverse problems, and data assimilation using experimental or simulation data.
• Co-design numerical algorithms and differentiation strategies for GPU parallelism, memory hierarchy, mixed precision, reduced synchronization, and distributed multi-GPU execution.
• Evaluate numerical accuracy, convergence, gradient quality, performance, and scalability on workloads in CFD, mechanics, thermal systems, and multiphysics.
• Identify promising methods and help move them toward reusable capabilities in CUDA-X libraries.

Skills

PDE algorithms

Numerical linear algebra

Iterative solvers

Optimization

Scientific computing

Education

PhD candidate

Tools

Python

C++

CUDA

Parallel computing

Job description

NVIDIA invites a PhD candidate to join a research internship focused on scalable differentiable algorithms. You will connect simulations with experimental data for calibration and data assimilation using AI/ML methods, evaluating on CFD, mechanics, thermal systems, and multiphysics workloads.

Collaboration across mathematics, code, hardware, and engineering applications is expected. If you enjoy cross-disciplinary work and advancing numerical methods on GPUs, this internship offers meaningful,