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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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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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. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification
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chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
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proficiency in oral and written English, creativity, thoroughness, and a structured approach to problem-solving Additional qualifications Experience with one or more of the following areas is meriting: Bayesian
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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
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occurrence patterns, mark behaviour, and interactions between events, to vary locally in space and evolve over time. The research will include model development, statistical inference and diagnostic methods
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application! Your work assignments The project's contribution will lie at the intersection of random matrix theory and statistical inference theory, with applications in several fields of science. Special
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described. The project has three main objectives: (1) Use simulation models to make inferences on the role of sex ratio selection on sex chromosome evolution in a different meiotic drive scenarios, (2
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. This four-year doctoral project will investigate biotic risks associated with birch and builds on research initiated during the first phase of Trees For Me. It combines two connected research tracks: damage