2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning
Core
Research and development of vehicle aerodynamic performance using computational fluid dynamics (CFD) and scientific machine learning to optimize vehicle design and efficiency.
Role type
Summer Research Intern (Vehicle Aerodynamics & Machine Learning)
Builds
Data-driven aerodynamic models, surrogate models, and automated engineering workflows for vehicle design optimization.
Domain
Automotive engineering, fluid dynamics, scientific machine learning
Deliverable
production ML models
Required skills
CFD simulation setup and post-processing, Python programming, machine learning model development and training, data analysis and preprocessing, aerodynamic flow field analysis, scientific computing
Preferred skills
Physics-informed machine learning, surrogate modeling, graph neural networks, design-of-experiments, high-performance computing, technical communication
Technologies
Python, NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, CFD/CAE/CAD software
Responsibilities
Set up and run CFD simulations for vehicle aerodynamic studies, develop and evaluate ML models for flow-field predictions, build datasets from simulations and experiments, automate simulation workflows with Python scripts, document research methods and results, collaborate with cross-functional engineering teams
Seniority
Intern