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Field
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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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large scientific collaborations by applying methods from social network analysis, large language models (LLMs), and knowledge graph technologies. As part of a small, supportive research team, you’ll have
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ability to work both independently and as part of a team Preferred Qualifications Experience in graph-based AI models, multi-omics data integration, or network inference Background in epigenomics, gene
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translating natural language specification into a symbolic representation (e.g. knowledge graph (KG) or logic program) and a symbolic solver computing the solution. Another example is the generation
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graph states by exploiting the entanglement between the spin state of a carrier trapped in a quantum dot and the polarization of the photons successively emitted by the dot. We are now able to entangle
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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them
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, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab , the 6GSPACE Lab , the HybridNetLab
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of electronic Hamiltonians. The postdoctoral researcher will develop graph neural networks based on the MACE architecture to predict Hamiltonian elements for 2D materials and van der Waals heterostructures, with
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associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train