Postdoc Researcher

Los Altos, CA; Cambridge, MA /
Materials Discovery – Accelerated Materials Design and Discovery /
At Toyota Research Institute (TRI), we’re working to build a future where everyone has the freedom to move, engage, and explore with a focus on reducing vehicle collisions, injuries, and fatalities. Join us in our mission to improve the quality of human life through advances in artificial intelligence, automated driving, robotics, and materials science. We’re dedicated to building a world of “mobility for all” where everyone, regardless of age or ability, can live in harmony with technology to enjoy a better life.

We strive to build a company that helps our people thrive, achieve work-life balance, and bring their best selves to work. At TRI, you will have the opportunity to enjoy the best of both worlds ‒ a fun environment with forward-thinking people who enjoy solving tough problems and the financial backing to successfully achieve our goals. Come work with TRI if you’re interested in transforming mobility through designing technology for safer cars, enabling the elderly to age in place, or designing alternative fuel sources. Start your impossible with us.

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 sophisticated computational materials modeling, new experimental data, artificial intelligence, and automation to significantly accelerate materials research in this area. Our focus is on developing tools and capability to enable this acceleration. In addition to the research work of the internal team, we collaborate closely with a dozen universities and national labs. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community more broadly.

We are looking for two postdocs who can help us with a new project for accelerating atomistic simulations of doped materials. You will draw from Density Functional Theory (DFT), machine learning (ML), and other computational methods to develop next-generation software for increasing overall simulation throughput by an order of magnitude. We will work closely with our experimental partners in Japan. We regularly get to present our work at conferences and within the Toyota family of companies, patent our innovations, and publish papers in high-impact journals.

We encourage you to join a creative team of a dozen scientists and engineers dedicated to enabling a sustainable future. We all grow working alongside other inspiring people and constantly learn new skills together at the interface of materials science and AI. You will combine your individual research with the rest of the teams’ and our collaborators’. Together with our collaborators, we have early access to comprehensive data streams, and the capabilities we develop provide them feedback to optimize their scientific workflows. We have a high degree of autonomy which gives us the privilege to select challenges to address, play to our strengths and pursue the solutions we believe in.

We are currently working from home, but when we get back in the office you can be working in our headquarters in Los Altos.

We'd love to hear from you if you:

    • Have a Ph.D. in physics, chemistry, materials science, or a related field completed before August 2021.
    • Have experience with DFT or other methods for atomistic simulations.
    • Are proficient at a general-purpose programming language (preferably Python).
    • Are comfortable using high-performance computing, cloud computing, or GPU-based platforms.
    • Have demonstrated interest in machine learning.
    • Are familiar with or want to learn practices for team-based software development.
    • Thrive in a culture that values diversity, collaboration, humility, and learning.

It’s a bonus if you:

    • Have research experience involving solid-state energy materials, surfaces, and defects.
    • Have worked with materials informatics, ML, and large datasets.
    • Have a strong research background, including peer-reviewed publications.
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