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Field
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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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high information content Flow MRI datasets with physics based modelling and Bayesian inference to determine constitutive models for non-Newtonian and other complex fluids in situ. The project will
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and genomic) data to address the most pressing health research challenges. The advanced analytics team specialise in the development and application of statistical methodology (including Bayesian
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subcellular mechanisms (proliferation, differentiation) with multicellular mechanical and biochemical interactions. Apply Advanced Statistical Methods: Perform Bayesian parameter estimation and identifiability
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subcellular mechanisms (proliferation, differentiation) with multicellular mechanical and biochemical interactions. Apply Advanced Statistical Methods: Perform Bayesian parameter estimation and identifiability
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, XRF, isotopic, and tephra analysis, alongside the construction of Bayesian age–depth models using radiocarbon, 210Pb, and tephrochronology. Candidates with experience in metagenomics (sedimentary aDNA
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, XRF, isotopic, and tephra analysis, alongside the construction of Bayesian age–depth models using radiocarbon, 210Pb, and tephrochronology. Candidates with experience in metagenomics (sedimentary aDNA
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Technology. Applicants should hold a doctoral degree in public health, epidemiology, or a related discipline, and have strong experience in longitudinal data analysis and advanced causal inference methods (e.g
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, XRF, isotopic, and tephra analysis, alongside the construction of Bayesian age-depth models using radiocarbon, 210Pb, and tephrochronology. Candidates with experience in metagenomics (sedimentary aDNA
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& Technology. Applicants should hold a doctoral degree in public health, epidemiology, or a related discipline, and have strong experience in longitudinal data analysis & advanced causal inference methods (e.g