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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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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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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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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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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators