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Field
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of material science, coatings technologies and advanced thermal spray deposition systems, this research explores the development of novel protective chemistries that can be deposited in situ to protect
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providing a structured, semantic framework that enhances knowledge sharing and data reuse across different platforms and systems. Project Aim This PhD will develop an ontology-based methodology to improve
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, working closely with Professor Nora Pashayan. The successful candidate will focus on developing ethnicity-specific risk thresholds that more accurately reflect the variations in breast, ovarian, and
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Although the development of systems architectures and models has been an established practice for many years, the assessment of the quality of models has relied on expert judgement with
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for postgraduate study at UCL: https://www.ucl.ac.uk/prospective-students/graduate/entry-requirements What we offer The studentship is full-time and will run for 4 years from September 2025. It covers full-time
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. Expected outcomes include: development of novel algorithms that significantly improve predictive accuracy for equipment failure; creation of scalable monitoring systems that reduce operational costs
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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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reducing odours from pomace and digestate. The project comprises seven work packages. As a leading partner, the University of Surrey will develop a system digital twin (SDT) to enhance overall sustainability
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can feed directly into precision surgery algorithms and clinical trials. Few PhD projects offer such a clear line of sight from variant to mechanism to clinical translation. Located on the thriving
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms