Energy & Materials Intern - Agentic Systems
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 in Automated Driving, Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics. 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 methodologies for AI agents to interact with scientific workflows, towards the end of improving collaboration with users. This project may include studying and improving agentic capacity to plan, design, and execute complex tasks within scientific and engineering domains. The intern will prove out the practical application of these systems, with the goal of producing and developing adaptable frameworks and advancing our understanding of agentic AI's contribution to scientific exploration and automation.
Requirements
- Currently enrolled in a doctoral program in computer science, applied mathematics, materials science, engineering, physics, chemistry, or a related discipline.
- Have familiarity with Large Language Models (LLMs) and agentic systems, and optionally fine-tuning LLMs, and Retrieval-Augmented Generation (RAG).
- Are proficient in computational workflow documentation and artifact generation.
- Have analytical skills for assessing agent performance, critiquing recorded workflows, and developing transparent evaluation metrics
- Demonstrated aptitude for applying AI concepts to practical scientific and engineering problems
- 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
Number of Employees
101-250 employees
