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Field
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Main description: Native mass spectrometry is an expanding structural biology tool to elucidate the function of protein complexes. Excitingly, the demand for this technology is increasing. However
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the entry requirements for the potential programme you are applying for. To Apply for this project please click on the following link -https://www.exeter.ac.uk/study/funding/award/?id=5734 Funding details
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capture, evaluate the potential apportionment of soil/sediment chemistry to sources and locations from established field experimental plots and catchments-valleys identified from baseline data using isotope
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: Search for the ‘Course Title’ using the programme code: 8201F The Research Area is: Physical Geography Select PhD Civil Engineering (full time) as the programme of study You will then need to provide
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Computational background: engineering, physics, maths, or computer science. How to apply: Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and a 300-word statement explaining your motivation
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benefit from the extensive and broad expertise in AI and biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a
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PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication
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Structural Biology, Immunology, Cell Biology, Neuroscience and Synthetic Biology www2.mrc-lmb.cam.ac.uk/students/international-phd-programme/projects/. PhD students at the LMB are affiliated with
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The Open University Business and Law Schools are inviting applications to join our campus-based PhD Programme and our new PhD by Distance Learning Programme. The starting date for both programmes is
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap