Sobre esta vaga de Accelerated Science Intern, Representation Learning for Molecules and Materials na Tri
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 advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
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’re looking for a researcher/engineer interested in how machine learning models represent molecules and materials, and whether different models discover the same underlying physical structure. In this role, you’ll collaborate with TRI researchers to investigate how training data, learning objectives, and model architectures shape learned representations. The work will combine mathematical reasoning with computational experiments to study when models arrive at shared representations, what information those representations capture, and how they connect to the physics of the systems being studied.
Responsibilities
Implement and evaluate machine learning models for molecules and materials using reproducible training and analysis workflows.
Design controlled experiments to study how learned representations vary across training runs, model architectures, and learning objectives.
Investigate the geometry and information content of learned representations and their relationship to physical quantities.
Develop evaluation methods to distinguish meaningful shared structure from similarities introduced by model parameterization or analysis choices.
Contribute high-quality, well-documented code to an internal codebase and help translate results into internal reports, publications, and/or patent disclosures (as appropriate).
Qualifications
Currently enrolled in a doctoral program in materials science, computer science, statistics, applied math, machine learning, physics, or a related discipline.
Strong foundations in machine learning, linear algebra, probability, and optimization, with demonstrated experience training models on real datasets.
Familiarity with representation learning or scientific machine learning; experience studying model convergence, learning dynamics, symmetries, or identifiability is a plus.
Proficiency in Python and modern ML tooling (e.g., PyTorch, experiment tracking, reproducibility best practices).
Ability to work collaboratively across disciplines and to translate research ideas into working code.
