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. Training will cover modelling, quantitative analysis, and laboratory methods. OBJECTIVES O1. Build a spatiotemporal lineage atlas of the pre-implantation human embryo. The student will assemble high-content
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the virtual embryo; and Bayesian Inference to calibrate model parameters and identify developmental control points. You will also gain experience in the simulation-experiment loop, where computational
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quality, avoiding the paralysis that troubles artificial algorithms when options seem equally good. This project asks: what objective functions do such biological systems optimise, and how can we use
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both Bayesian and frequentist methods), and creating automated statistical reports and other relevant output from within statistical software. Application & interview 5 Track record of publications in
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at the University of Sheffield. Background & Motivation: Stroke is one of the leading causes of death and disability in the UK, affecting hundreds of thousands of people every year. Early detection is critical, as
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and neural network methods will be used to transfer diagnostic capability between structures in a population. Bayesian approaches will also be emphasised. The Research Associate will take a leading role
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. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms to capture
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require the use of cognitive shortcuts. - To develop and fit computational models (e.g., reinforcement learning, Bayesian models) to participant data, allowing for a precise, quantitative definition of
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple