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-analytical workflows, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore
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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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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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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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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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. 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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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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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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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