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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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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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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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of economics and development. Position You will work actively on the preparation and defence of a PhD thesis in the field of economics and development. The PhD project will use theory and survey experiments
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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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students who are eager to develop and apply artificial intelligence techniques and mechanistic mathematical models to explore fundamental questions in biology. The PhD program is organized in partnership
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students who are eager to develop and apply artificial intelligence techniques and mechanistic mathematical models to explore fundamental questions in biology. The PhD program is organized in partnership
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Your profile Master's degree in computer science, Mathematics, Applied Mathematics, Computer Engineering, Software Engineering, Data Science, Information Systems (Engineering), or related fields with a
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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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with all aspects of the project. Researchers will be encouraged to work on several aspects of the present project (for instance a specific experimental implementation and the underlying theory thereof