Energy & Materials Research Intern, AMDD

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 capability to enable this acceleration. We collaborate closely with a dozen universities and national labs and our colleagues in Japan. 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 offering a 12-week summer internship for graduate students nearing the completion of their PhD. We are looking for interns who can help us apply state-of-the-art machine learning model architectures to problems related to the scientific design of materials for electrochemical devices. Currently, we need assistance in bridging the gap between simulation and experiment.

You will help incorporate scientific theory or intuition into new machine learning models that provide actionable input to experimentalists. You will collaborate with software engineers and researchers to translate your work into prototype tools that can be used by the wider community. This is a unique opportunity to contribute to cutting-edge research, present your findings at internal meetings, and potentially co-author papers or patents.

Qualifications

• Enrolled in a PhD in physics, chemistry, chemical engineering, materials science, computer science, or a related field, nearing completion of your degree
• Have a strong research background, including peer-reviewed publications
• Are proficient with machine learning tools such as TensorFlow or PyTorch
• Have experience with model architectures, including graph neural networks, self-attention mechanisms, and transformers
• Possess effective written and verbal communication skills
• Thrive in a culture that values diversity, collaboration, humility, and learning