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Schoonjans (Pediatric Neurology Department UZA) are seeking a highly motivated PhD student to work on clinical data analyses and multi-omics approaches in the rare genetic neurodevelopmental syndrome, STXBP1
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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that can find both short- and long-term time dependencies without overfitting and requiring large amounts of data. The methodologies will be developed and validated on complex real-world mechatronic systems