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, please state the course code EGEGR3 with Project: Data-Intensive AI Thermodynamic Models for Next-Generation Building Decarbonisation with Prof Dongfang Liang. Please note that there is a £20 application
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’. Use ‘Course Search’ to identify your programme of study: Search for the ‘Course Title’ using the programme code: 8040F The Research Area is: Civil Engineering (Water Resources) Select PhD Civil
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, and epibenthic biodiversity. The project will build on a working prototype, the Neural Network Enhanced Marine Observation system, a low-cost, shallow-water, edge-AI-enabled spatial camera system
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rest, will be combined with detailed in vivo human physiology techniques in healthy volunteers. Methods may include stable isotope tracers, blood and muscle biopsies, imaging (ultrasound, MRI, DXA), and
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financial economics. You will work at the frontier of interdisciplinary research, using high-resolution flood models alongside property data to build a dynamic picture of where flood hazards are concentrated
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fluorescence microscopy, live cell imaging, and cell proliferation and migration assays (such as scratch assay, high throughput migration assay, 3D migration, wound contraction assay). To apply, please follow
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research, as appropriate to the discipline, through open data, open code, open educational resources and practices that support replication. Informal enquiries related to the advertised position, planned
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experience. Desirable Attributes Experience working with quantitative and textual data, writing computer code, and/or using existing Digital Humanities tools. The successful candidate will be supported to fill
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code, like the plastic ends of shoelaces, called telomeres. When cells grow and divide the DNA is incompletely copied, shortening the telomeres. As part of a normal ageing process, when the telomeres
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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