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JOB TITLE: Differentiable Simulation Research Intern - Summer 2027
As a Differentiable Simulation Research Intern - Summer 2027, you will join a research team focused on scalable differentiable algorithms, specifically adjoint and automatic differentiation methods. You will prototype GPU-native solvers and integrate AI-based methods with simulation algorithms for parameter calibration, inverse problems, and data assimilation. Your daily work involves co-designing numerical algorithms for GPU parallelism, memory hierarchy, and distributed multi-GPU execution while evaluating accuracy and scalability on CFD, mechanics, thermal systems, and multiphysics workloads. You will utilize Python, C++, CUDA, PyTorch, JAX, PETSc, Trilinos, MFEM, libCEED, and OpenFOAM. The role addresses the technical challenge of connecting simulations with experimental data for calibration, ultimately moving promising methods toward reusable capabilities in CUDA-X libraries for researchers and engineers.
Prototype GPU-native solvers using adjoint and automatic differentiation techniques for engineering simulations.
Integrate AI-based methods with simulation algorithms for parameter calibration and data assimilation.
Co-design numerical algorithms and differentiation strategies for GPU parallelism and distributed multi-GPU execution.
Evaluate numerical accuracy, convergence, and performance on CFD, mechanics, and multiphysics workloads.
Identify promising methods to develop reusable capabilities in CUDA-X libraries.
Design computational experiments to evaluate numerical and performance tradeoffs.
What we're looking for
Pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or an equivalent program.
Strong foundations in algorithms related to PDEs, numerical linear algebra, iterative solvers, optimization, or scientific computing.
Research experience with adjoint methods, automatic differentiation, inverse problems, data assimilation, or gradient-aware simulation approaches.
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 technical results clearly.
Experience developing, scaling, or analyzing GPU solvers, including multi-GPU or distributed execution (preferred).
Hands-on CUDA experience and use of profiling, benchmarking, or performance analysis to improve numerical algorithms (preferred).
Experience with NVIDIA Warp, CUDA-X, PyTorch, JAX, PETSc, Trilinos, MFEM, libCEED, OpenFOAM, or related frameworks (preferred).
Python
Rust
C++
PyTorch
Deep Learning
Computer Vision
Multi-GPU Training
Git
LLMs
VLMs
Multi-modal Foundation Models
AI Model Training
Software Development
Neuroscience
Cognitive Science
Python
C++
CUDA
PyTorch
JAX
ROS2
Isaac Sim
Isaac Lab
MuJoCo
Reinforcement Learning
Imitation Learning
Computer Vision
Sim-to-Real
Robot Foundation Models
Warp
Deep Learning
Model Training
VLAs