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of Hull before continuing their PhD research at the University of Sheffield. Offshore wind turbines are critical to the UK’s transition to net-zero, but their reliability is challenged by harsh marine
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conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
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staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic
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Sklodowska-Curie Doctoral Network linking 21 academic, cultural, and industrial partners to develop advanced nondestructive evaluation and data-driven digital tools for paintings and 3D artworks (https
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generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. Want
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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
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The successful candidate will join a global network of Simons Postdoctoral Fellows in Strong Gravity and Black Holes that are part of the Simons-BHSG. This new, multidisciplinary and multinational collaboration
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conservation, using participatory modelling approaches (e.g. Causal Loop Diagrams, Bayesian Belief Networks) that bring together diverse knowledge systems. Co-create locally meaningful scenarios of coastal
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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