Differentiable Simulation Research Intern - Summer 2027

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.
• What we need to see
• Pursuing a PhD in applied mathematics, scientific computing, computational engineering, computer science, mechanical engineering, aerospace engineering, or equivalent program area.
• 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, gradient-aware simulation approaches, or a related area.
• Experience writing numerical software in Python and C++, with familiarity with parallel or GPU computing.

Languages