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Field
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A funded 4-year UK EngD / PhD studentship is available in the group of Prof Sandy Knowles within the School of Metallurgy and Materials at the University of Birmingham, with a tax-free stipend of
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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in spin-dependent processes in materials and devices for energy technology. More information can be found on the group website: taitgroup.web.ox.ac.uk . Please contact Dr Claudia Tait (claudia.tait
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, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data, disambiguate historical agents, and contribute
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to treat and prevent due to the ability of C. difficile to produce robust spores that can survive most cleaning regimens. Since the COVID-19 pandemic, rates of CDI have increased in the United Kingdom, but
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol
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reconciliation, enhancement, and integration of the MLGB dataset, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data
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been identified. Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships
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practice. Digital Image Correlation (DIC) is a well-established, non-contact optical technique used to measure motion and deformation. It provides comprehensive full-field deformation data, essential
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to that data will be strictly controlled. More information relating to the manner in which we process your personal data is located within our privacy notice for staff, job applicants and others working