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activities across these decentralised and increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project
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should have strong digital signal processing and mathematical backgrounds evidenced by grades and/or prior publications. Additionally, the candidate should have expertise or strong interest (evidenced by
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and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection, transaction classification, and
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sites. We will subsequently use information theory and (or) wavelet analysis to link this data with environmental variables and identify their principal drivers. Functional forms of LUE-WUE and gc will be
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one-fits-all model was proven unsuccessful. Large Language Models (LLMs) and knowledge graph models are expected to harmonize the formats and semantics but there are many open questions about their
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interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering
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character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer
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theory for liquid crystal flow as well as theory for viscoelasticity of polymeric liquids. Both in the practical and simulation/theory work you will be guided and supported by post-docs and senior
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mathematical backgrounds, evidenced by grades and/or prior publications. Additionally, the candidate should have expertise or a strong interest (evidenced by familiarity with fundamental concepts) in several
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Science, Applied Mathematics, or Physics with an electromagnetic background Strong theoretical knowledge in some of the following areas: Radar Systems and Signal Processing Optimization methodologies Machine