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: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post-Doctoral associate will be based at NYU Abu Dhabi and will directly report to Prof
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developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and neural networks. The key challenge? Designing robust and stable numerical schemes that remain
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numerical solution a serious computational challenge. This project aims to tackle that head-on by developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and
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optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques to quell opto-mechanical
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needed to guarantee user-defined error bounds of reachable sets for nonlinear and hybrid systems. This project will exactly close this research gap: We will develop essentially new methods to ensure
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to precisely shift microbiomes to desired metabolic states. Our research combines multiplexed measurements of single cells, populations and ecosystems with concepts from nonlinear dynamical systems, control
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(microelectromechanical systems) devices for X-ray optics at synchrotron radiation sources. Some background of the project is given in the publications listed below. The idea is to make highly nonlinear MEMS-based
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algorithms, mechatronics, intelligent robotics and prosthetics, robot learning algorithms, foundations of machine intelligence, as well as nonlinear control and systems theory. Furthermore, we offer unified