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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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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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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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. Our research brings together social theory, empirical studies guided by a range of interpretive methodologies, and the critique as well as advancement of interventions and social policies. We
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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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familiarity with fundamental concepts) in several of the following topics: Signal processing for wireless communication. Solid background in optimization and information theory. Good understanding
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large-scale MD simulations, ideally with LAMMPS, demonstrated via corresponding roles in publications Experience with density functional theory (DFT) calculations, ideally with VASP, demonstrated via
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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