Human Interactive Driving Research Intern, Learning-Based Control for Safe Autonomous Driving

This is a paid 12-week internship opportunity at Toyota Research Institute (TRI), a hybrid, in-office role at the headquarters in Los Altos, CA. The intern will join the Extreme Performance Intelligent Control team, developing techniques to advance vehicle safety and performance using machine learning, optimization, and control theory. The internship focuses on research combining machine learning with physical system knowledge and control theory to develop reliable controllers for autonomous vehicles, including learning-based control methods that enforce forward invariance, stability, and constraint satisfaction. The intern will work towards a publication with professional researchers.

Responsibilities

Work with researchers to develop and then undertake a well-scoped research project in a relevant area; Extend existing controls/AI/ML algorithms or design new algorithms for control at a vehicle's performance limit; Explore approaches to formalize guarantees of safety for machine learning algorithms; Implement algorithms on a full scale vehicle research platform; Publish original research at a high impact conference

Requirements

Ph.D. or M.S. student in relevant fields (ML/AI, robotics, computer science, mechanical engineering, aerospace engineering, or similar); Proficient in scientific Python, Unix, and robotic frameworks (e.g. ROS2.0); Knowledge of machine learning, nonlinear system control, and optimal control; Experience working on joint scientific and engineering projects

Nice to have

Knowledge of vehicle dynamics; Familiarity with or interest in racing or high-performance driving; Relevant publications in the fields above; Good collaboration skills; Links to relevant open-source contributions or technical project write-ups

Benefits

Medical, dental, and vision insurance; Paid time off benefits (including holiday pay and sick time)