Postdoctoral AI Fellow, Post-Training for Materials Discovery

The Team

The goal of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to enable the materials breakthroughs needed for truly emissions-free mobility. We combine AI, autonomy, experimental data, and computational modeling to pursue a step change in the speed and scale of materials discovery. We collaborate closely with colleagues at universities, national labs, and global Toyota. Together, we develop capabilities designed for real-world impact in both Toyota’s industrial applications and open research more broadly.

The Opportunity

We are looking for a postdoctoral AI Fellow to define an original research direction in model post-training, using materials discovery as a real-world testbed. This appointment is anticipated to be for one year, with an option to extend another year by mutual agreement. You will take ownership of problem framing through validation and play a leading role in shaping the research as it evolves. No materials background is required, only the curiosity to learn from domain experts and uncover where AI can make a meaningful difference.

As part of AMDD, you will work closely with materials scientists, ML researchers, and software engineers to translate your ideas into capabilities that can inform real-world decisions. The fellowship is also intended to include an academic co-mentor in AI, with the specific collaboration developed with you after you join TRI, subject to institutional arrangements. Publications and conference presentations are encouraged and expected. Projects may also draw on internal datasets relevant to Toyota use cases. Ultimately, the goal is to pursue fundamental advances in AI while addressing important scientific and industrial challenges.