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embedding graph-based problems, particularly those known to be challenging for classical computing architectures. Some of your responsibilities will include: Design and develop mixed-signal circuits
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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop
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: algorithmics, graph transformation and algorithm engineering. Exposure to systems chemistry or systems biology is an asset but not a must. Proven competences in programming and ease with formal thinking are a
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) Research area: Large Language Models (LLMs), knowledge graphs (KGs), commonsense knowledge Tasks: foundational or applied research in at least one of the following areas: LLMs, KGs, knowledge extraction
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graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset management. Job description The increasing accessibility of data in
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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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Apply and develop advanced multimodal data tools and knowledge graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset
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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