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

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