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have: a PhD or equivalent qualification (or be nearing completion thereof) in Materials Science/Engineering or another subject relevant to the study, development and/or application of nanostructured
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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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Credit and Qualification Framework level 12 (PhD) plus track record of emerging independence within a research/professional environment, or alternatively possess professional qualifications and experience
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uses, improving the AI and MRI algorithms, and linking them with information from biological studies on tumour tissue. This project harnesses AI to improve diagnosis and clinical decision-making leading
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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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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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-holomorphic Hilbert Modular Forms”. The central aim of the project is to develop explicit algorithms for computing with non-holomorphic Hilbert Modular Forms and using these algorithms together with theoretical
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using a variety of advanced methods with the support of project supervisors. The postholder will have completed a PhD in a relevant discipline and have expertise in quantitative research methods
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. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences