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. This role will involve developing and applying analysis plans using a variety of advanced methods with the support of project supervisors. The postholder will have completed a PhD in a relevant discipline and
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-leading database of MRI images of childhood tumours and have developed AI approaches to diagnose different types of tumour. To be useful for patients, this needs to be delivered in hospitals in real time
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. There are opportunities to broaden out into other areas such as new algorithm development, and advanced computational methodologies for integrated analyses. You will have a key role in planning, designing and executing a
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. There are opportunities to broaden out into other areas such as new algorithm development, and advanced computational methodologies for integrated analyses. You will have a key role in planning, designing and executing a
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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Research Fellow in Intervention Development to join the Big Data in Health Grou About us Our big data in health team at the University of Southampton is based in the Primary Care Research Centre. We
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We are seeking an outstanding, creative researcher with the skills to develop novel, ‘artificially intelligent’ approaches to the application of nanofabrication techniques – see, for example https
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for this role. This role will involve developing and applying analysis plans using a variety of advanced methods with the support of project supervisors. The postholder will have completed a PhD in a relevant
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change to delivery of health services. Experience with large datasets and excellent communications skills will be essential for this role. This role will involve developing and applying analysis plans
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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing