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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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translational medicine using a "bench-to-bedside" approach. By harmonising and analysing diverse biomedical data, while focusing on the secure data processing and predictive modelling, we aim to drive progress in
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to collaboration with industrial partners (Airbus) and have a strong interest in aerospace modelling projects. The candidate must hold a PhD in mechanical engineering, aerospace engineering or a related field. The
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research, - a high degree of autonomy and creativity in problem-solving, - openness to collaboration with industrial partners (Airbus) and have a strong interest in aerospace modelling projects
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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for quantized and pruned neural networks, creation of quantized and pruned demonstration models, reproduction of state of the art, experiments in heterogeneous quantization Depending on expertise
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required. Evaluating the actual impact of these strategies on grid performance and system reliability necessitates detailed power flow analyses based on realistic distribution network models. Accordingly
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well as interactomics and kinetic translatomics of translating ribosomes and their genetic manipulation in dopaminergic neurons from various PD models. The aim is to use these findings to identify translationally