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 Mission Within TRI’s Human-Centered AI Division, the Harmonious Communities team aims to understand and improve wellbeing in complex systems such as workplaces, organizations, and communities. Our goal is to develop new ways of measuring, modeling, and shaping wellbeing—leveraging AI to support better outcomes at both individual and collective levels. The Challenge Wellbeing in real-world systems is complex, and shaped by interactions across individuals, teams, and environments. Many drivers are difficult to observe, measure, or model, and traditional approaches often fail to capture these dynamics. We are exploring how AI—combined with systems thinking—can help uncover hidden patterns, identify key drivers, and enable the design of interventions or system changes that improve outcomes at scale. The Team The Harmonious Communities team is an interdisciplinary group of machine learning researchers, behavioral scientists, and human-computer interaction experts. We work across fields to study how wellbeing emerges, evolves over time, and can be influenced through organizational structures, technologies, and policies. Our work connects fundamental research with real-world impact within Toyota. The Opportunity We are looking for a creative and self-driven intern to explore how AI can be used to better understand and improve wellbeing in human systems. This is an open-ended, research-oriented internship where the project will be shaped collaboratively based on your interests and ideas. You will have the opportunity to propose and develop novel approaches, including machine learning, causal learning, natural language processing, or agent-based simulations, to study complex social systems.
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Career Level
Intern
Education Level
No Education Listed
Number of Employees
101-250 employees