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Field
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, while simulations are subject to error due to uncertainty in nuclear data and unresolved physical processes e.g. thermal expansion and fine-scale inhomogeneities. Generating independent simulation
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atomisers. To ensure high quality measurement data, enabling meaningful correlations, measurements will be performed on a simplified prefilming geometry, with aero and hydrodynamic features representative
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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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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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
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will be randomised to receive either standard of care (which includes a patient information sheet) or standard of care plus a nurse-led phone contact. Objectives: 1) To determine patients’ perspective on
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-based reviews, data collection and analysis, written outputs, and the dissemination of research findings to different audiences, including through investor briefings and academic publications. We
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automated rankings. The research includes real-world validation using university admissions data and will contribute to the broader fields of AI in education and ethical decision-making. This PhD research
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phone number, one of which must be your most recent line manager. For information about how your personal data is used as an applicant, please see the section on Applicant Data on our HR web pages - https
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological