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increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection
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. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains. The position is hosted in the Leibniz Junior Research Group
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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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research documentation, including ethics and funding applications Assist with routine team administration Assist with preparing reports, graphs, figures and presentations on research outcomes at different
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interdisciplinary research and training program. The objective of the open PhD position is to advance current over-the-air-computing (AirComp) approaches for federated and graph-based Embedded AI to account for
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the summer months. Qualifications Basic Qualifications: Previous experience working outdoors for full days Previous experience with data entry and graphing with spreadsheet software such as excel Some prior
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XAI methods, e.g. counterfactuals in reasoning and knowledge graphs (KGs) based on domain expertise, to strengthen inferences drawn from data, and to reduce complexity of learning – by factual reasoning
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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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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains