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. This project will use large-scale metagenomic mining to uncover novel phage-host associations and identify phage-encoded enzymes, such as endolysins, with antibacterial activity. By integrating evolutionary
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propagate through bacterial communities while deactivating AMR genes. However, current designs are limited by scalability and complexity. This project aims to overcome these limitations by integrating large
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large herbivores and off-road driving, using the RPI to control for climate-driven variability and incorporating data from allied ground monitoring. This should reveal landscape-scale recovery timeframes
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promising targets for antiviral drug development. While the COVID-19 pandemic highlighted the threat of RNA viruses, large DNA viruses such as African Swine Fever Virus (ASFV) remain underexplored despite
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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PhD Studentship: Nanopore Technology for Rapid and Accurate Measurement of Antibiotic Concentrations
environments. Training and Student Development: The student will gain hands-on experience in: Molecular biology and aptamer engineering Nanopore fabrication and single-molecule sensing Data acquisition and
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intensity of these changes. This PhD project will ultimately enable aircraft to reroute safely and efficiently in real time as weather evolves. By merging scientific machine learning, large-scale data
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loads in power system dynamics and stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven
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2026. Please make sure you detail within the application form the programme you wish to be considered for – see School information below. You must include the title of the studentship in your application
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simulation workload and update the solver data structures when the mesh changes. These approaches would be applied on modern large-scale heterogeneous parallel computing environments where both CPUs and GPUs