Accelerated Science Intern, Representation Learning for Molecules and Materials

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.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
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
We are seeking a research intern to develop algorithms and computational systems for multi-agent learning and coordination. The central question is how agents with different data and capabilities can share information, build predictive models, and select computational tasks that improve their collective understanding of data. Potential approaches include distributed and federated learning, as well as information-theoretic methods for communication and task selection. The intern will implement and benchmark these methods, evaluating how coordination affects model accuracy, communication costs, and computational efficiency. Relevant interests include machine learning, multi-agent systems, distributed computing, and information theory.

Employment type
Intern

Work arrangement
Hybrid