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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm
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more sensitive and faster cancer imaging. This PhD project will focus on surface functionalisation of metascintillators to optimise their scintillation performance, light yield, timing resolution, and
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experimental and modeling approaches. Experimental investigations will be conducted at the University of Manchester, utilizing established rigs to simulate a spectrum of conditions, from single-phase heat
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation system
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workflow for the imaging of Mg-LPSO alloys via X-ray nanotomography followed by higher resolution imaging of the identified regions of interest using transmission electron microscopy and atom probe
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collaboration to combine their expertise. By integrating these complementary approaches, we aim to provide the first comprehensive picture of Hsp90's structure–dynamics–function relationship, with broad
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-performance computing and imaging facilities. a collegial, international atmosphere and flexible, family-friendly working hours a structured PhD programme with extensive training and career-development
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of analysis and visualization software Very good written and spoken English skills are required Furthermore, experience in dealing with the ICON model and the scientific utilization of ICON data is an advantage
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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laboratory and imaging facilities Access to a full range of equipment and laboratory facilities Inclusive and supportive lab culture with flexible working practices Applications welcome from candidates from