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Field
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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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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 17-Oct-25 Location: Irvine, California Type: Full-time Categories: Academic/Faculty Physical Sciences Internal Number
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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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European consortium, we bring together industry partners, municipalities, academia, learned societies, and patient organisations to cocreate, pilot, evaluate, and iterate a series of city-level strategies
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factors are involved. This thesis project focuses specifically on the first part of this research. The successful candidate will be responsible for conducting a series of psychophysical experiments using a
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and as part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This
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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