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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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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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graphs. The prototype you will develop using IDP-Z3 will be integrated with results from the other groups to deliver a tool that can benefit both from expert knowledge (your part) as well as from data (U
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-regulatory networks. Following cis-GRN network reconstruction and formal graph analysis, we will identify key regulatory factors governing cell-type specific response to CMT-causing mutations. Finally, we will
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integrate these data into the cis-regulatory networks. Following cis-GRN network reconstruction and formal graph analysis, we will identified key regulatory factors governing cell-type specific reponse to CMT