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are processed in burned soils is crucial for predicting environmental impacts and informing effective wildfire management. The PhD student on this project will investigate how microbial communities mediate
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focus on the medical image processing aspect of the Birth4Cast simulator by researching and developing automated image segmentation procedures to extract the pelvic floor muscle complex and the fetal head
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evolutionary process of resistance, rendering many existing treatments ineffective. To combat this crisis, there is an urgent need to discover new bioactive compounds, particularly antibiotics, many of which
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for demanding terrestrial and non-terrestrial (Aerospace) applications accounting for sources of variability and uncertainty, for example, those arising from material, fabrication process, and boundary and
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methodology to generate confidence in such decision, potentially reducing maintenance costs and down-time for offshore wind energy production. The images taken by each drone are loaded into the pre-processing
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flows, and to have developed skills in experimental fluid mechanics, statistics, data processing, machine learning, and mathematical modelling. Supervisors: Dr Kostas Steiros Duration: 3.5 years. Funding
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods
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identification of deterioration processes and assessment of their evolution/extent. Please reach out to the primary supervisor, Prof. Craig Hancock , if you have any questions. Entry requirements: We are looking
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power consumption. Many emerging biomedical devices, such as non-invasive and indwelling systems, require reliable operation and continuous feedback on biochemical conditions at the device–tissue
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Project Outline Skeletal muscle undergoes progressive loss of mass and function during ageing, a process termed sarcopenia. This decline arises from reduced myogenesis and degeneration of existing