ML For Materials Characterization Intern

Key Skills

Algorithms, Analytical and Problem solving, Framework, PyTorch, TensorFlow

Job Description

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 Human-Centered AI, Human Interactive Driving, Energy and Materials, Machine Learning, and Robotics.

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.

Qualifications

• 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.

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