2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning
Salary not listed • Warren, Michigan, United States of America • Internship
Posted 1d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
Job Location
Warren, Michigan, United States of America
Tech Stack
Remote Work Policy
On-site
Employment Type
Internship
Categories
Machine Learning Engineer
About the job
As a 2027 Summer Intern on the Vehicle Aerodynamics and Machine Learning team, you will work at the intersection of physics-based vehicle simulation, computational fluid dynamics, scientific machine learning, and aerodynamic design optimization. You will support research that uses CFD and AI/ML to improve vehicle aerodynamic performance, accelerate design exploration, and enable more efficient development of next-generation vehicles. The work may include analyzing vehicle flow fields, developing data-driven aerodynamic models, evaluating design concepts, and translating research results into practical engineering tools. This role is intended for an inquisitive, technically motivated student who enjoys applying engineering fundamentals and modern computational methods to complex problems. You will work with experienced researchers and cross-functional engineering partners while developing your skills in vehicle aerodynamics, simulation, machine learning, data analysis, and technical communication.
Responsibilities
- Support vehicle aerodynamics research involving external flow, aerodynamic drag, lift, downforce, cooling airflow, and vehicle efficiency.
- Set up, run, monitor, and post-process CFD simulations for vehicle and component-level aerodynamic studies.
- Prepare simulation geometry, meshes, boundary conditions, design variables, and analysis workflows.
- Analyze aerodynamic coefficients, pressure distributions, velocity fields, wake structures, flow features, and other performance metrics.
- Build and curate datasets from CFD simulations, experimental results, vehicle geometry, and design variables.
- Develop, train, test, and evaluate machine-learning or surrogate models for aerodynamic quantities and flow-field predictions.
- Investigate physics-informed, data-driven, reduced-order, graph-based, or other scientific machine-learning approaches relevant to vehicle aerodynamics.
- Apply data preprocessing, feature engineering, dimensionality reduction, model selection, hyperparameter tuning, and validation techniques.
- Evaluate model accuracy, robustness, generalization, uncertainty, and limitations across vehicle geometries and operating conditions.
- Compare ML predictions with CFD, wind-tunnel, experimental, or other trusted engineering results.
- Support design-of-experiments, surrogate-based optimization, sensitivity studies, and aerodynamic shape exploration.
- Develop Python-based scripts, tools, and workflows to automate simulation setup, data processing, model training, visualization, and reporting.
- Help identify opportunities to reduce analysis time and enable earlier aerodynamic feedback during vehicle design.
- Document assumptions, methods, data sources, model configurations, results, limitations, and recommendations.
- Create clear plots, visualizations, technical reports, presentations, and demonstrations for engineering and research audiences.
- Collaborate with researchers and cross-functional partners to define technical questions, review results, and recommend next steps.
- Present technical findings and participate in innovation, networking, and professional-development activities.
Requirements
- Pursuit of an advanced degree (MS or PhD) in mechanical engineering, aerospace engineering, computational engineering, computer science, applied mathematics, physics, data science, or a related technical discipline.
- Able to work fulltime, 40 hours per week during the summer months.