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Numerical simulations of Lattice QCD DoS Dr. Craig McNeile (craig.mcneile@plymouth.ac.uk , tel.: +441752586332) 2nd Supervisor Dr. Vincent Drach ( vincent.drach@plymouth.ac.uk , tel: +441752586335
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framework, and numerical implementation will allow the construction of a faithful ‘digital-twin’ of these alloy-oxide systems for wide applicability in surface science applications; though initially focussing
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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lack of CFD-grade experimental data for reliable validation of numerical methods. In parallel to the CFD research, the Thermo-Fluids group at the University of Manchester is developing a novel modular
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impacted by sewage release and is associated with significant flooding events. The student will combine experimental methods with metagenomics and bioinformatics to profile the threats associated with
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clinical or environmental sample we can rapidly identify all organisms present including novel strains and newly emerged pathogens. Currently such methods are only available in specialist laboratories, each
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background in Computer Science, Mathematics. Students with interests in machine learning, deep learning, AI, uncertainty quantification, probabilistic methods are encouraged to apply. For eligible students
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Research theme: Formal Methods How many positions: 1 This 3.5 year PhD is funded by the Department of Computer Science at The University of Manchester. The successful candidate will receive
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PhD studentship, University of Bradford, Faculty of Health Studies Application Deadline: 16 June 2025 Project Title: Triage transitions: A mixed methods evaluation of NHS ambulance call
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer