About the role
Position Overview
The Computational Modeling Team develops advanced engine and propulsion technologies using state-of-the-art digital tools, applying AI and simulation-based product development to accelerate the design of clean, efficient, and sustainable transportation technologies. The graduate intern will work alongside research scientists to advance AI-driven automation within our CFD-in-the-loop design optimization framework, contributing ongoing projects.
Education and Qualifications
- Currently enrolled in a graduate program (MS or PhD) at the time of application and throughout the internship
- Computer Science, Mechanical or Automotive Engineering preferred
- Authorized to work in the U.S. or able to obtain authorization by the program start date (CPT/OPT approval required where applicable)
Preferred Skills and Experience
- Programming proficiency in Python; familiarity with C/C++ a plus
- Hands-on CAD and geometry modeling expertise (e.g., SolidWorks, CATIA, NX, or open-source CAD kernels); meshing and CFD pre-processing experience
- Design of experiments, surrogate modeling, Gaussian processes, Bayesian optimization, active/adaptive learning, and optimization algorithms
- Exposure to CFD tools (e.g., CONVERGE, ANSYS Fluent, STAR-CCM+) and HPC environments
- Experience with machine learning frameworks (PyTorch, scikit-learn) and LLM-based coding agents
- Strong analytical, documentation, and communication skills; able to work collaboratively in a team research environment
Opportunities eligible for internship course credit (credits earned), please check with your Academic Advisor or University.