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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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, health and medicine, logistics, infrastructure, and industrial operations. The PhD project will focus on the intersection of machine learning, causal inference, and classical statistical methods. A central
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causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
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theory, statistical inference, and probabilistic modelling for uncertainty quantification in deep learning, particularly large language models. The focus will be on quantifying and evaluating uncertainty