ML For Materials Characterization Intern

Toyota Research Institute

Toyota Research Institute

Software Engineering, Data Science
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.
Energy and Materials
The Energy and Materials Division at TRI is building tools to accelerate the design and discovery of new materials, fostering a transition to more sustainable mobility. Our research applies AI, data-driven methods, and automation to materials science, and spans the atomic to the device scales. 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 AI4Science community, and aim for publication in high-impact conferences/journals and conferences.
The Internship
Scientists use characterization techniques to identify materials’ structure and other properties in the lab. This project focuses on using advanced machine learning techniques to solve the intricate challenges associated with materials characterization data. We aim to explore sophisticated model architectures that can precisely map structural representations, such as 3D coordinates (CIF structures) and text-based chemical formulas, to characterization data in 2D array formats, such as X-ray Diffraction (XRD), X-ray Absorption Spectroscopy (XAS), and Raman spectral data. The objective is to build an invertible generative model capable of performing spectral refinement (denoising, peak identification, etc.) and solving the characterization inversion problem, thereby advancing our ability to deduce material structures from their spectral data.


  • Are currently enrolled in a doctoral program in STEM subjects (materials science, computer science, chemistry, applied math, statistics, chemical engineering, or a related field)
  • Proficiency with AI and machine learning frameworks, such as TensorFlow or PyTorch
  • Experience with developing transformer models, diffusion models, or multi-modality models

Bonus Qualifications

  • Familiarity with such domain-specific tools or software as Pymatgen and ASE for handling and analyzing materials data
  • Knowledge of materials characterization techniques such as XRD
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.