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at the intersection of neural engineering, computational neuroscience, biomedical signal processing, and embedded systems design. The project aims to develop a novel electrical nerve-stimulation
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works have shown that for embedding hierarchies, we should abandon Euclidean geometry altogether and operate in hyperbolic space [1]. Our lab has published multiple papers showing that hyperbolic deep
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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pseudorange correction through multiobjective optimisation. The research will explore multiple classes of constraints that will be embedded as objectives: Internal pseudorange consistency: ensuring
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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About the Project The future power grid will be a highly complex cyber-physical system, integrating multiple distributed energy resources (DERs) such as solar, wind, marine, and bioenergy alongside
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: The political economy of housing climate adaption, led by dr. Jens van ’t Klooster and dr. Cody Hochstenbach. This is an interdisciplinary project embedded in the Department of Political Science and the
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issue is the embedding of hierarchies, for which a different geometry is better suited, namely hyperbolic geometry. Seminal works have shown that for embedding hierarchies, we should abandon Euclidean
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of research include integrating AI-enhanced weather forecasts with crop models, exploring tools to guide land allocation and nitrogen management, or developing dashboards that fuse multiple data streams