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on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real life settings with a state-of-the-art wearable device (https
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differential equations relevant to computational fluid dynamics. These efforts might include Bayesian physics-informed neural networks and neural operators. Bayesian neural networks for approximating piecewise
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-dimensional Bayesian inverse problems for image reconstruction and chemical reaction neural networks with sparsity-promoting (and edge-preserving) priors, including diffusion-based approaches. Neural solvers
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on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real life settings with a state-of-the-art wearable device (https
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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
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is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta-Structures for Adaptable, Resilient and Sustainable Renewable Energy Power Plants), a prestigious Marie Skłodowska-Curie Doctoral
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possible date, preferably by 1st April 2026. This is a full position (100%) limited to 3 years. This PhD position is available within the EU-funded Marie Skłodowska Curie Doctoral Network on Low Data Machine
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. Developing a new Bayesian, data-driven approach for multidisciplinary geophysical time series analysis to detect anomalies in real time, useful for disaster management managers managing a monitoring network
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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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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models