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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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physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
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research objectives and proposals for own and/or collaborative research area. • Prepare papers for publication in leading journals and/or contribute to the dissemination at national/international
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outputs in this area with increasing degrees of autonomy. The group at CIRA studies a variety of accreting compact objects, from X-ray binaries to tidal disruption events, seeking to understand how
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will apply predictive, Bayesian modelling for predicting performance of microbial consortia based on mass transfer, metabolic pathways and proteomic analysis. The work plan will also include developing