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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis
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emerging areas, and currently covers the following topics: Signal and image processing theory Statistical signal processing, non-stationary processes, Bayesian inference, signal models, sampling theory
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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involve developing methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large-scale graph data structures. Experience in statistical
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). The position is funded by NSF-EPSRC grant 'Stochastic Shape Processes and Inference', in collaboration with the University of Nottingham, Ohio State University, and Florida State University. The successful
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). The position is funded by NSF-EPSRC grant ‘Stochastic Shape Processes and Inference’, in collaboration with the University of Nottingham, Ohio State University, and Florida State University. The successful
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high