Energy & Materials Intern – Carbon Neutral Strategy

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 Automated Driving, Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavior Models, and Robotics.

The Energy and 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 Carbon Neutral Strategy Program operates at the intersection of technology, business, and policy, using tools like energy systems modeling and ML to inform decarbonization strategy development across Toyota. Our projects often involve collaboration with scientists from universities and national labs. Interns will be involved in industrial research on topics of broader interest to the general materials science and clean energy community, and several previous intern projects have resulted in peer-reviewed publications in journals such as npj Computational Materials and Chemical Science and presentations to internal Toyota leadership to support strategic decision-making.

We are looking for an intern researcher to contribute to our energy systems research program. Together, we will scope and develop in-house analytical tools to inform how different policy mechanisms, behavioral assumptions, and technology pathways could influence the pace of vehicle turnover and the future vehicle powertrain mix. We welcome you to join a unique team of scientists and engineers where you will constantly learn new skills at the interface of energy and AI.

• Incorporating human behavior heuristics into models.
• Evaluating alternative policy and program designs.
• Quantifying the total cost of ownership for different vehicle types under a range or assumptions.
• Applying machine learning methods to understand parameters that help achieve desired system outcomes.

• Currently enrolled in a masters or a doctoral program in STEM subjects (e.g. materials science, chemistry, chemical engineering, mechanical engineering, computer science, systems engineering, public policy, energy economics, or a related discipline with an emphasis on computational research).
• Experience in Python.
• Experience with ML (e.g. supervised learning, unsupervised learning, reinforcement-style decision models).
• Comfortable working with scientific computing and ML frameworks such as Sci Py, PyTorch, or Tensorflow.
• Demonstrate interest in decarbonization, technoeconomics, and policy analysis.

• Familiarity with GIS.

The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, business or organizational needs, market location, job-related knowledge, skills, and experience. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time).

Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

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