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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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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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the Research Group Nonlinear Optimization and Inverse Problems (Head: Prof. Dr. D. Hömberg) starting as soon as possible. The position is within the Math+ project "Anisotropic microfluids -- fluctuations
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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