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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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, and health economics Macroeconomics, policies, and institutions in open economic systems Spatial, urban, regional, international, and industrial economics Applied time series econometrics Finance
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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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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
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measurement; Measurement of related tracers (e.g., Radon); Programming (e.g., R, Python) for advanced atmospheric time-series analyses, including machine learning; Skills for presenting research at scientific
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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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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in