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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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Characterisation" "Data Science and Machine Learning in Materials" "Plastics Recycling and Circular Economy" Research theme: "Materials Characterisation" "Data Science and Machine Learning in Materials" "Plastics
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to the new theoretical development of graph deep learning, and contribute to the research community of brain connectivity and network neuroscience. This is a joint Phd studentship between Coventry (UK) and
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD