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Optimization in Generative Models Intern
Toyota Research Institute
Cambridge, MA, USA
Posted on Saturday, November 18, 2023
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.
TRI's Optimization team develops novel optimization algorithms and software to enhance all aspects of Toyota's business. Our current focus is on algorithms for training and guidance of generative diffusion models, with applications in vehicle design. In particular, we are interested in adapting generative modeling techniques that underpin image generation tools like Stable Diffusion and applying optimization techniques to control outputs based on physical engineering constraints.
We are looking for a motivated intern to pursue new avenues of research consistent with the team's mission. This is an opportunity to apply your knowledge to novel research questions and work towards a publication with professional researchers. The internship will be in our Cambridge office and includes competitive compensation befitting the exciting, but fun, nature of the research work at TRI. Applicants with relevant publications in the fields above and good collaboration skills are highly encouraged to apply.
- Develop and deploy optimization algorithms for controlling and constraining outputs of generative models
- Design and implement experiments for evaluating performance of these algorithms
- Publish basic research related to these tasks
- Ph.D. or MS candidate in Electrical Engineering, Computer Science, Operations Research, or related field
- Experience with vision models
- Experience with 3D representations
- Designing and training neural networks
- Convex optimization
- Mathematical background in numerical linear algebra
Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.
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TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.