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collaboration, and who have experience in the following areas (with the first being the most important): Semantic web technologies and knowledge graphs (RDF, SPARQL, SHACL, etc.) Large language models (LLMs
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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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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
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covariate adjustment of multivariate outcomes in clinical trials, causal machine learning, exponential random graph models for modeling mpox, demography and infectious disease epidemiology. You will