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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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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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systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups around the
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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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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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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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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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-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas dynamics in urban environments. Gas dynamics shape air
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas