Energy & Materials Research Intern, Generative Materials Modeling

Key Skills

GIS, Machine learning techniques, PyTorch, SciPy, Supply Chain Management, TensorFlow

Job Description

The Energy & 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 Team

The Carbon Neutral Strategy Program operates at the intersection of technology, business, and policy, using tools like energy systems modeling 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 presentation to internal Toyota leadership to support strategic decision-making.

The Internship

We are looking for an intern researcher to contribute to our energy systems research program. Together, we will scope and develop technoeconomic systems analysis frameworks to inform technology and business development surrounding topics like predicted policy compliance requirements around materials usage or forecasting placement of electric vehicle charging infrastructure. These models may involve incorporating human behavior heuristics, material supply chain limitations and circularity, prioritizing disadvantaged communities in the energy transition, or multiple critical decarbonization technologies, including EV charging and hydrogen refueling infrastructure, biofuels and synthetic fuels, plug-in hybrids, etc. 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.

Qualifications

• Currently enrolled in a Masters or a PhD 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 with ML: supervised learning: neural networks (e.g. classification, LSTM), Unsupervised: Clustering (e.g. k-means, hierarchical), and/or reinforcement (e.g. Markov decision process, discrete choice model)
• Experience with platforms like SciPy, PyTorch, Tensorflow; GIS is a bonus
• Demonstrated interest in decarbonization, technoeconomics, and policy

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.

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