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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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, 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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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set