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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools
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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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the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from
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, you will be able to choose up to two research labs for which you wish to apply START YOUR APPLICATION Are you ready to prepare an outstanding application? Join our online workshop to learn how to find
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, and two exchanges. A special element will be the design of EXPLORA kits: through teamwork with other doctoral candidates in the network you will make an EXPLORA kit forming a meaningful contribution
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international conferences and to collaborate with other academic and industrial partners. The ideal candidate holds a Master’s degree in Robotics, Control Theory, Mechanical Engineering, or Applied Mathematics. A
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We’re looking for a motivated PhD student with a Master’s in Robotics, Control Theory, Applied Math, or Mechanical Engineering eager to work on model-based control of soft robots Job description We
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are collaborating in ScaDS.AI on advancing a broad spectrum of AI. The spectrum of available topics ranges from core areas of AI Theory, Hardware, Mathematic foundations, AI applications to societal aspects of AI
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to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity to contribute to foundational research at the intersection of control theory