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
This internship opportunity falls within the Adaptive Behavior Systems team of Human-Centered AI Division (HCAI). We are an integrated team of researchers with backgrounds in machine learning, the behavioral sciences, and human-computer interaction. At the core of our work, we aim to create technologies that prioritize human-centered outcomes and enable purposeful human futures.
The Internship
We are seeking a PhD research intern to join our Adaptive Behavior Systems team to push the boundaries of LLM-based human modeling and simulation. The intern will collaborate with our interdisciplinary team to study how language-based generative machine learning models represent human intentions and behavior and develop methods for evaluating how well these models capture individual differences and diverse human perspectives. The project will be focused on a specific sub-area of the overall research program. The intern will further engage in strategy discussions about how the research connects to business impact at Toyota.
This is a Summer 2027 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.
In collaboration with your mentor and the other researchers in the department, you will:
Our goal is that you end the internship with work that informs internal strategy and may be publishable in an academic journal or conference.
Develop algorithms and computational systems for multi-agent learning and coordination, enabling agents with different data and capabilities to share information, improve predictive models, and use computational resources efficiently.
Plan and conduct human-robot interaction research to understand how people communicate preferences to robots, using multimodal interaction studies and sharing findings through presentations and publications.
Research electrocatalysis models that connect atomistic simulations with experimental systems, using materials science, data analysis, and reasoning methods to support catalyst design and model translation.
Develop physics-informed machine-learning methods and computational mechanics models to improve scientific simulations, analyze experimental and computational data, and publish research findings in collaboration with multidisciplinary.
Conduct supervised undergraduate research in quantum information science, using experimental, theoretical, or computational methods to gather and analyze data and present findings in quantum computing or sensing.
