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apply: send covering letter, CV and academic transcripts by email to Geoffrey.Rivers@nottingham.ac.uk . Applications without academic transcripts will not be considered. Please refer to the project title
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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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Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts by email to Geoffrey.Rivers@nottingham.ac.uk . Applications without academic
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possess or expect to obtain, a 2:1 or first-class degree in Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts by email to
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. Candidates must possess or expect to obtain, a 2:1 or first-class degree in Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts
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. Candidates must possess or expect to obtain, a 2:1 or first-class degree in Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts
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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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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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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