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Modernising Medical Microbiology (MMM) unit at the University of Oxford (https://www.expmedndm.ox.ac.uk/mmm). You will be joining a highly interdisciplinary team of approximately 40 clinicians, computational
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work with limited supervision to design and accurately execute experiments to achieve the goals of the project. Applicants should hold, or be close to completion of, a PhD in Biology or a related subject
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to design and accurately execute experiments to achieve the goals of the project. Applicants should hold, or be close to completion of, a PhD in Biology or a related subject. You should have a high level of
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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mission, including publications and grant development. Applicants should hold a PhD/DPhil in Biochemistry, Biophysics, Chemistry, or a related discipline, or have equivalent relevant experience. You must
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sample collection, processing (including blood sample preparation, biopsy processing, and molecular techniques), analysis, storage, and distribution tasks. Aspects of this work are associated with patient
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system with integrated sensors. You should hold or be near completion of a PhD/DPhil with relevant experience in the field of robotics, biomedical engineering, information engineering, electrical
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computing and associated increases power demand and procurement costs. This role will support the creation of a network of experts, a team of champions and a portfolio of community projects to provide UKRI
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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hepatitis and liver disease. This post is funded by the National Institute for Health and Care Research (NIHR) as part of a significant research programme that leverages large-scale healthcare datasets