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disciplines to address the global societal challenges that threaten us today, climate change without doubt being the most important. Our focal subjects: sustainable aerospace, big data and artificial
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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and respirometry, in healthy controls and patients with ME/CFS. You will collect, analyse and curate these multi-modal datasets. Your will write several scientific publications about the collected data
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subjects: sustainable aerospace, big data and artificial intelligence, bio-inspired engineering and smart instruments and systems. Working at the faculty means working together. With partners in other
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and networks worldwide to become a truly global centre of knowledge. This 4-year PhD position is embedded in the Artificial Intelligence department at the Faculty of Science and Engineering and in
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range of science and engineering disciplines, from classical disciplines such as mathematics, astronomy and mechanical engineering, to interdisciplinary fields such as artificial intelligence,nanoscience
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of infrastructure networks, which remain fragmented and hinder the coordination needed to address cross-sectoral implications of demand changes (e.g., modal shifts, the use of home batteries). At the same time
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devices. Yet, predicting and exploiting nonlinear behaviour, particularly the role of modal couplings in energy transfer and dissipation, remains a fundamental challenge. This PhD project offers a unique
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, particularly the role of modal couplings in energy transfer and dissipation, remains a fundamental challenge. This PhD project offers a unique opportunity to tackle these two complementary perspectives. In
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Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in