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Human Interactive Driving Intern – World Models
Toyota Research Institute
Human Interactive Driving Intern – World Models
This job is still taking applications, but it's been up a while.
About the job
Toyota Research Institute develops AI, robotics, and materials sciences to improve human life. The internship focuses on research in autonomous driving, machine learning, and creating world models using real and simulated data.
Requirements
- Enrolled in Ph.D
- program
- Strong in machine learning
- Experience with world models
- Knowledge of simulation environments
Qualifications
- Ph.D
- in CS, Robotics, or ML
- Proficiency in Python and PyTorch
- Experience with large datasets
- Published in top-tier conferences
Full 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 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!
Responsibilities
- Conduct original research in one or more areas: world modeling, multi-agent interaction, reinforcement learning, perception, or simulation-to-reality transfer.
- Collaborate closely with full-time researchers on the design, training, and evaluation of learning-based driving systems.
- Contribute to building and experimenting with task-aware, multi-modal, and uncertainty-aware models.
- Develop and evaluate prototypes in closed-loop simulation environments and, time permitting, on high-performance autonomous driving hardware.
- Present research findings through internal talks and work towards a top-tier academic publication.
- Integrate and work with large-scale datasets (open-source and internal).
Qualifications
- Currently enrolled in a Ph.D. program in Computer Science, Robotics, Machine Learning, or a related field.
- Strong background in machine learning, particularly in areas such as deep learning, generative models, reinforcement learning, or probabilistic modeling.
- Demonstrated experience with one or more of the following: World models (e.g., latent dynamics, diffusion-based models), Model-based RL or decision-making, 3D perception or sensor fusion, and Large-scale simulation for robotics or autonomous systems.
- Prior publication(s) in top-tier conferences (NeurIPS, ICLR, ICML, CVPR, ICRA, CoRL, etc.)
- Proficiency with Python and PyTorch.
- Familiarity with AWS services (S3, EC2, and SageMaker) and open-source driving datasets (nuScenes, Waymo, Argoverse, etc.) is a plus.
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