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accepted all year round Details Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control (e.g. model predictive control
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successful candidates will develop fundamental theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization
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theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization, Position 2 on adaptive policies, and both will
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uncertainty-aware, optimization-based motion planning at the control and planning layer. You will develop scalable trajectory optimization and real-time Model Predictive Control (MPC) strategies that use the