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out without a solid theoretical foundation. This leads to unpredictable behavior, which is a major bottleneck for efficient and resilient control of complex systems in engineering and medicine. Your
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complex and dynamic environments. The research is driven by applications where current technology is insufficient, such as large-scale biodiversity monitoring in natural environments, smart agricultural and
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. The goal is to enable efficient, robust, and high‑quality situational awareness in complex and dynamic networks, thereby laying the foundation for future autonomous and data‑driven systems. The specific
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is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view
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Control is successfully conducting research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control
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Control is successfully conducting research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control
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well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive
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research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division
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such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes