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Ultrafast lasers drive innovations from quantum technology to medical imaging, yet controlling femtosecond pulses remains a major challenge. Metamaterials are artificial structures with
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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- and time-specific innervation that extends into adolescence. Our lab has used whole-brain tissue clearing, light-sheet imaging, and machine learning to map the spatial and temporal dynamics of serotonin
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THR demand in younger patients expected to increase fivefold by 2030, revision surgeries will also rise. To improve implant positioning, image-guided navigation is increasingly used in complex THR
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to Newcastle Portal Once registered select ‘Create a Postgraduate Application’. Use ‘Course Search’ to identify your programme of study: search for the ‘Course Title’ using the programme code: 8208F leave the
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: Select ‘Postgraduate Research’ in the 'Type of Study' field Select ‘Full Time’ in the ‘Mode of Study’ field Select ‘2025’ in the ‘Year of Entry’ field Enter programme code 8420F in the ‘Course Title’ field
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programme of study: · search for the ‘Course Title’ using the programme code: 8208F · leave the ‘Research Area' field blank · select ‘PhD Energy
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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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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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candidate with experience in cell culture, stem cells, molecular biology, immunohistochemistry, biochemistry, protein purification and/or confocal imaging. Experience in using human tissue is advantageous. A