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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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on performance, safety, and robustness of robotic and learning-enabled systems. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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of the structure of power distribution networks and the impact of LCTs on network capacity Experience of formulating optimization problems and solving using appropriate methods Understanding of the potential use
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systems for both learning and dynamical systems. This work will include new approaches to online learning of optimal policies for hierarchical cyber-physical systems, mixing components of learning and
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aspects include rough paths and subsequent developments for nonlinear stochastic partial differential equations. The theory of signatures and rough volatility also provides important connections to algebra