Energy & Materials Research Intern, Generative Materials Modeling

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 Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.

This is a summer 2025 paid 12-week internship opportunity in Los Altos, HQ. Please note that this internship will be an in-office hybrid role.

Energy & Materials

The Energy & Materials Division at TRI is building tools and creating strategies, from accelerating the design, discovery, and deployment of new materials to performing interdisciplinary systems analysis to help foster a transition to more sustainable mobility. Our research applies AI, data-driven methods, and automation to materials science and device engineering, along with stakeholder-informed systems modeling.

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 sophisticated computational materials modeling, new experimental data, artificial intelligence, and automation to significantly accelerate materials research in this area. Our focus is on developing tools and capabilities to enable this acceleration. We collaborate closely with a dozen universities and national labs and our 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

We are looking for an intern researcher to explore strategies for conditional generation of materials structures. Towards the end of better understanding synthesis processes, we want to explore what kinds of information can be used to condition generative models for proposing new crystal structures which can be the jumping off point for future first-principles or ML-powered molecular dynamics studies.

Your day-to-day will include integrating cutting-edge ML models for structure generation, developing model architectures, and making requisite model modifications for the problem at hand. We welcome you to join a unique team of scientists and engineers where you will constantly learn new skills at the interface of materials science and AI.

Qualifications

• Currently enrolled in a PhD program in STEM subjects (computer science, machine learning, statistics, chemistry, chemical engineering, materials science, or a related discipline)
• Experience with Python
• Work with generative structure modeling, including but not limited to diffusion models, for chemistry or materials
• Experience with computational materials science