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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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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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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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from a technical and scientific perspective. The Ph.D. candidate will develop methods for uncovering DNS infrastructure dependencies at scale, relying on active measurements and graph-based analysis
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known as kernelization) for a wide range of NP-hard graph problems. Please state your entry requirements plus any necessary or desired background A first class or an upper second class British Bachelors
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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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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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, specifically modelling the complex interrelations among infrastructure, human operators, and organizational structures using dynamic graphs, system dynamics, Agent Based Models, and discrete event simulations
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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