Behavior Modeling Intern - (HCAI and E&M)

Toyota Research Institute

Toyota Research Institute

Los Altos, CA, USA
Posted on Saturday, November 18, 2023
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 Robotics, Human-Centered AI, Human Interactive Driving, and Energy & Materials.
This is a Summer 2024 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.
The Mission
This internship is a joint opportunity between TRI’s Human-Centered AI (HCAI) and Energy & Materials (E&M) divisions. HCAI is an integrated team of ML researchers, behavioral scientists, and human-computer interaction experts who aim to support people to make better decisions by using the best of big data, technology, and insights about why we do what we do. E&M is an interdisciplinary team of materials scientists and ML researchers striving to accelerate the discovery of advanced materials for future mobility.
The Team
This specific internship is co-hosted between HCAI’s Carbon Neutrality and E&M’s AMDD departments. HCAI’s Carbon Neutrality department is an interdisciplinary team of researchers working on tools and methods to enable consumers to contribute to carbon neutrality through Toyota products. E&M’s AMDD department is focused on advancing Toyota’s transition to carbon neutrality through accelerated materials design and discovery.
The Internship
Our HCAI and E&M teams are committed to using innovative modeling and simulation tools to discover and develop technologies that help reduce our impact on the environment. We are looking for an intern to help develop integrated models of human behavior that assess and simulate the potential impact of different technologies on carbon emissions. As part of this project, you will have the opportunity to help build accurate and explainable models of different vehicle-related behaviors (e.g., driving, charging) and integrate these models with existing models of different technologies (e.g., batteries) and external data sources (e.g., grid emissions). The ultimate goal of these integrated models will be to perform simulation studies to assess the emission-reducing potential of different behaviors and future technologies, which could have real-world social and business impacts.
As a joint intern between HCAI and E&M, you will gain exposure to research in industry settings and have the opportunity to interact with a large group of researchers from a variety of backgrounds (e.g., behavioral science, materials science, ML, HCI). Over the course of the project, you will also have the opportunity to participate in broader TRI events and activities and network with researchers from other teams. We welcome you to join a positive, friendly, and enthusiastic team of researchers, where your research will help contribute to a cleaner, more environmentally friendly future.

Responsibilities

  • Scope the project to align to core research efforts
  • Be the primary driver of the technical plan (e.g., model development, conduct experiments on datasets) with regular feedback from mentors
  • Implement the project using TRI resources
  • Present the project’s approach and findings in verbal and written communications
  • Our goal is that you end the summer with work that is publishable in an academic journal or conference

Qualifications

  • Pursuing a PhD degree in Computer Science, Machine Learning, Materials Science, Applied Mathematics, Environmental Studies, Systems/Control Engineering, Psychology or Sociology (with a focus on large-scale modeling), or related field
  • Experience with at least one of the following is preferred: large-scale systems modeling (e.g., models of driving, crowd behaviors), time series forecasting, electrochemical models, generative models and simulation
  • Experience working on multidisciplinary collaborative projects
  • Strong proficiency with python or other general purpose programming language
  • Experience with causal modeling and/or generative adversarial networks (GANs) is a plus but not required
  • Track record of performing research projects from ideation through publication
  • Desire to work on challenging open-ended research projects
  • Demonstrated ability to work autonomously while soliciting feedback
  • Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position
Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.
The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package including vacation and sick time. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.
TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.