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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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-to-failure dataset is then fed into powerful Artificial Intelligence algorithms, particularly time-series Neural Networks. These models learn the complex sequence of events that reliably precedes performance
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models of the additive manufacturing process. Surrogate models to accelerate virtual material testing and verification will be developed to allow the generation of property distributions from process
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senior postdoc to characterise the epigenomic landscape at time points when the immediate (innate) immune response has subsided, but the immune memory persists. Integrating these data with resistance
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uncertainty about the effectiveness of a drug using a “prior” probability distribution, before the trial is conducted. This distribution is used to assess the likelihood that a trial will produce a successful
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, including teeth grinding and normal everyday movements, ensuring the accuracy and reliability of the collected data. Developing, training, and validating state-of-the-art machine learning algorithms
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interact with the world around us. However, the power requirements and carbon emissions of AI are equally dramatic: training a single state of the art algorithm has the same carbon footprint as the lifecycle
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, algorithms, and applications’, Information Fusion, 81, 2022. [2] A. Z. Wang et al, ‘Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis’, IEEE Trans. Visual
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(KBAs). However, these networks are largely based on current species distributions. As climate and habitats change, species ranges are shifting—posing a fundamental question: will today’s conservation
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. This is a thriving and expanding group of geometers and mathematical physicists, with currently 8 permanent faculty, and a healthy number of postdocs and PhD students. Our interests broadly encompass