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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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develops new intellectual understanding Disseminate research findings for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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to Tackle Modern Slavery’, led by Dr Rowland Seymour. The successful candidate will join a diverse and friendly research group with PhD students and a Research Software Engineer. The goal of the project is to
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understanding Disseminate research findings for publication, research seminars etc Supervise students on research related work and provide guidance to PhD/MSc students where appropriate to the discipline Develop
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development of mathematical models and algorithms for the analysis of biopharmaceutical manufacturing processes with a focus on assuring safety and alignment of machine learning models with the expected
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regular team meetings Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods as
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for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and
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and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute
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-related activities and research-related administration. Undertake management/administration arising from research. Supervise students on research related work and provide guidance to PhD students where