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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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extrapolating loads along the substructure, for any type of turbine, given specific geometric, inflow and sea-state information. Furthermore, such a machine learning surrogate can speed-up both design and
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will transition in a second phase to white box approaches that result in interpretable models. For ground truth data, μCT data will be used. A similar approach will be applied using surface roughness
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mobility of migrants shape local populations? Developing a spatial microsimulation model of population dynamics with application in infectious disease modelling (DynaMIGs)”. DynaMIGs is a four-year
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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. Clinical studies have demonstrated that hereditary neuropathies can also involve muscle pathologies, complicate diagnosis and hamper therapy development. We have developed advanced in vitro cell models
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in AI: Generative Diffusion & 3D/4D Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Semantic-Aware Compression & Network Information Theory
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. Clinical studies have demonstrated that hereditary neuropathies can also involve muscle pathologies, complicate diagnosis and hamper therapy development. We have developed advanced in vitro cell models
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are linked to research on composite hydrogen tanks, composite propellers for drones and finite element modelling of textile manufacturing. All research will be conducted with leading companies in
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electrophysiology to translational models, including animal studies and analyses of human tissue samples. This full-stack methodology enables us to directly link molecular channel function with disease phenotypes