Energy & Materials Intern - Probabilistic Programming

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.

The Team

The long-term vision of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to accelerate the development of truly emissions-free mobility. Realizing this vision will require the discovery of new materials and devices for batteries, fuel cells, and more. Our aim at TRI is to merge cutting-edge computational materials modeling, experimental data, artificial intelligence, and automation to significantly accelerate materials research. Our focus is on developing tools and capabilities to enable this acceleration. We collaborate closely with a dozen universities and national labs and colleagues across global Toyota. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community more broadly.

The Internship

We are looking for an intern to contribute to the development and/or benchmarking of our probabilistic programming tools. Together, we will develop tools that provide insight into the design and testing of fuel cells, electrolyzers, or related devices. Your day-to-day may include: probabilistic programming, time-series analysis, structural causal model development, and/or machine learning model development. We welcome you to join a unique team of scientists and engineers where you will constantly learn new skills at the interface of materials science and AI.

Qualifications:
• Currently enrolled in a doctoral program in computer science, statistics, applied math, machine learning, or a related discipline.
• Have experience with probabilistic programming or probabilistic machine learning applied to time series modeling.
• Have experience with standard machine learning tools in Python such as PyTorch, JAX, etc.
• Bonus: Have experience with structural causal modeling, causal inference, causal learning, and/or experience with chemistry or material science.

Please add a link to Google Scholar to include a full list of publications when submitting your CV for 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. 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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