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.