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are suitable. The aims of this project are to Review operating characteristics proposed for rare disease trials Develop novel Bayesian operating characteristics for different types of rare disease trials Apply
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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(steve.clifford@ncl.ac.uk ) or Dr Rebecca Hill (rebecca.hill@ncl.ac.uk ) Find out more about the Newcastle University Centre for Cancer here: https://www.ncl.ac.uk/cancer Find out more about the Faculty of Medical
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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Almost all radar systems currently transmit from the same location. This kind of radar modality has been optimised for decades, however the challenge to detect very small and very fast objects
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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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modalities and populations. This project (SpyGlass) will investigate machine learning methods, particularly generative and foundation models, to discover robust correlation structures in cross-modal datasets
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methodological approach through three key objectives, focusing on generations, intergenerational practice and policymaking and programmes in community sport for development. respectively. A fourth objective
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved