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filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
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signs of cardiovascular changes, adaptively model physiological patterns, and identify predictive biomarkers of maternal health. You will develop and apply cutting-edge techniques in: Signal processing
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research group, which leads pioneering work in multi-sensor navigation, signal processing, and system integrity for aerospace, defence, and autonomous systems. The research will deliver a comprehensive
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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at the interface between stochastic modelling, signal processing and data science. Ultimately, the project will develop key indices that can be used to assess the health of the soil ecosystem. Such indices
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Proteins are key biomarkers, indicative of normal biological or pathogenic processes and responses to intervention. Identification and quantification of such molecules in biological fluids is
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the interpretability of these models can be enhanced to support clinical decision-making. This project will leverage the complementary expertise of both supervisory teams in EEG signal processing, graph deep learning
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Position Summary: Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Control and Power Research Group, Department of Electrical and Electronic Engineering
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Arginine is an amino acid involved in numerous biological functions including cell proliferation, cell signalling, muscle contraction, immunity, neurotransmission, vasodilation and the synthesis
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reflection. Our previous work has only just reached the point where all the above elements have been successfully combined [1-2]: a working multi-scale design process combining theory and numerical simulations