Energy & Materials Intern - Generative Models and Reinforcement Learning
About The Position
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences. The Team The long-term vision of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to accelerate the development of truly emissions-free mobility. Realizing this vision will require the discovery of new materials and devices for batteries, fuel cells, and more. Our aim at TRI is to merge cutting-edge computational materials modeling, experimental data, artificial intelligence, and automation to significantly accelerate materials research. Our focus is on developing tools and capabilities to enable this acceleration. We collaborate closely with a dozen universities and national labs and colleagues across global Toyota. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community more broadly. The Internship This project aims to develop models and policies to aid the discovery of new materials and material representations. The project may include developing models and policies based on materials science-relevant action spaces to discover new materials, synthesis routes, and interpretable heuristics. In addition to model and policy development, the project may also include the development of pipelines for relevant dataset generation. This is a Summer 2026 paid 12-week internship opportunity. Please note that this internship will be an in-office role.
Requirements
- Currently enrolled in a doctoral program in computer science, materials science, engineering, applied mathematics, physics, chemistry, or a related field.
- Experience or familiarity with diffusion models and diffusion policies.
- Experience or familiarity with reinforcement learning.
- Experience with high-performance computing and high-throughput data pipelines.
- Please add a link to Google Scholar to include a full list of publications when submitting your CV for this position.
Benefits
- TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time).
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
