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Machine Learning-Guided Discovery and Experimental Validation of Novel Antimicrobials Against Pseudomonas aeruginosa (S3.5-MPS-Soukarieh)
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to statistics including; 1:1 support for students, a programme of statistics related workshops, collaborative work with academic departments, and the development of high quality online and face-to-face learning
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complex, involving multiple partners, working on innovative technology to provide practical solutions to industrial problems. As a member of the multi-functional project team, you will contribute
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Overview The Engineering and Maintenance team provide a reactive and planned programme of works for every building within the university, working in collaboration with other university departments
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Electroweak measurements with the ATLAS Experiment. School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr K Lohwasser, Dr C Anastopoulos Application Deadline
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the Research Programme Manager, academic and University-wide administrative colleagues as part of a team. You will be responsible for providing a high-quality administrative support service to all aspects
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Next generation processing methods for lithium-ion batteries (S3.5-CMB-Smith)
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engineering subject such as Computer Science, Aerospace Engineering, or Physics. If English isn’t your first language, you’ll need an overall IELTS score of 6.5 with at least 6.0 in each section Eligibility
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Next Generation Photonic Integrated Circuit Chips for Quantum Technology (C3.5-MPS-Faruque)
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Materials at Sheffield (SMASH) research team in the School of Chemical, Materials, and Biological Engineering. This project constitutes part of the 5-year BuildZero programme , a £6 million project funded by