2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning

GM•Warren, MI

9d•Hybrid

About The Position

As a 2027 summer intern on the Vehicle Aerodynamics and Machine Learning team, you will support research at the intersection of physics-based vehicle simulation, computational fluid dynamics, scientific machine learning, and aerodynamic design optimization. The work is intended to improve vehicle aerodynamic performance, accelerate design exploration, and support more efficient development of next-generation vehicles. Activities may include analyzing vehicle flow fields, developing data-driven aerodynamic models, evaluating design concepts, and translating research results into practical engineering tools. You will work with experienced researchers and cross-functional engineering partners while building skills in vehicle aerodynamics, simulation, machine learning, data analysis, and technical communication. The Energy and Propulsion Systems Research team conducts applied research to improve vehicle efficiency, performance, range, cost, and sustainability, collaborating with vehicle-development organizations, engineering groups, universities, technology partners, and industry leaders. The multidisciplinary environment may include aerodynamicists, CFD specialists, data scientists, machine-learning researchers, vehicle designers, optimization experts, and engineers across propulsion, thermal, body, chassis, and vehicle-performance organizations. The internship may also include innovation challenges, industry-leader interactions, cross-functional networking events, technical presentations, and community-service activities. The role is hybrid and requires reporting to Warren, Michigan at least three times per week, or at another frequency dictated by the business. Start dates are May 24, 2027, and June 14, 2027. GM does not provide immigration-related sponsorship for this role; applicants who will need such sponsorship now or in the future should not apply.

Requirements

Pursuing 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 full-time, 40 hours per week during the summer months.
Will not require GM immigration-related sponsorship now or in the future.

Nice To Haves

Coursework, research, or project experience in fluid mechanics, vehicle aerodynamics, computational fluid dynamics, turbulence modeling, numerical methods, optimization, or heat transfer.
Experience with Python, MATLAB, C++, or comparable programming languages.
Experience with NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or similar scientific-computing and machine-learning libraries.
Familiarity with CFD, CAE, CAD, meshing, visualization, Linux, high-performance computing, or engineering software workflows.
Experience developing or applying machine-learning models to scientific, engineering, physical-simulation, or time-series data.
Familiarity with surrogate modeling, physics-informed machine learning, graph neural networks, neural operators, transfer learning, uncertainty quantification, or model explainability.
Experience with design-of-experiments, parameter studies, optimization, or automated engineering workflows.
Ability to evaluate data quality, identify modeling limitations, and distinguish physically meaningful results from numerical or data artifacts.
Experience communicating technical results through reports, presentations, plots, code documentation, or demonstrations.
Strong analytical, written, and verbal communication skills.
Ability to work independently while collaborating effectively with researchers and cross-functional engineering teams.

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, and 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 machine-learning 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 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.