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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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random and deterministic systems and point configurations. Potential applications include lattices, point processes, random matrices, and random walks on groups. During the project you will work
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, financial economics, and financial econometrics. Economics at Newcastle is a thriving research community, with regular seminars from internal and external speakers, and a dedicated PhD seminar series. PhD
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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
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reporting of flood events. Using GIS, AI, and time series analysis, you will reveal how climate risks and housing dynamics intersect and how patterns may evolve under climate change. Leicester and
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(Beraza and Rushworth) whose work have a strong translational aim. This team will train the PhD student in a series of preclinical in vivo models; molecular biology and immunology methodologies; and complex
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growing financial exposure. When floods hit, they disrupt loan repayments, reduce property values, and undermine financial stability. Yet current stress-testing frameworks often overlook short-term
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bioremediation assays to identify new plasmid regulatory genes and determine how they manipulate bacteria. This knowledge will enable them to design, build and test a series of synthetic biodegradation plasmids
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impacts of barrier winds and tip jets in current and future climates via time-slice comparisons from state-of-the-art climate model simulations. Training You will use observations from a series of Norwegian