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In this PhD project, you will develop methods for the analysis, verification, and design of neural-network-based controllers. Rather than treating safety and stability as properties to be assessed only after training, you will investigate safety- and verification-by-design approaches that incorporate certifiable properties directly into the controller architecture and learning process.
This project offers a unique opportunity to work at the intersection of machine learning and control theory. You will develop rigorous theory and scalable computational methods for certifying closed-loop safety and stability, with potential directions including control barrier and Lyapunov functions, quadratic constraints, semidefinite programming, and neural-network architectures with intrinsic stability or robustness properties. You will also investigate how physical insight, prior system knowledge, and stabilizing baseline controllers can be combined with learning to improve performance while retaining rigorous guarantees. The developed methods will be evaluated on benchmark problems and more realistic scenarios involving complex dynamical systems.
No internships available at the moment.