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of complex phenomena in materials and at solid/gas and solid/liquid interfaces. You will join a large, international and interdisciplinary research group that provides a collaborative and supportive
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expected. Excellent knowledge in dealing with complex statistical models and methods and the willingness to support the team in statistical questions are also expected. The candidate should have teaching
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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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complex systems to identify fundamental security and privacy problems and researching and developing solutions that go beyond just short-term problem solving. Our team currently consists of 23 scientific