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for success: A PhD awarded in a relevant discipline e.g. social sciences, history, science and technology studies, anthropology, or other discipline with a strong background in conducting qualitative research
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, economic viability, and robustness to realistic operational uncertainty. PhD (or equivalent) in control engineering or closely related discipline. Track record in at least two areas: model predictive control
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Funding: School of Computer Science Studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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You will contribute to a programme of research, funded by the Horne Family Charitable Trust, which aims to increase the evidence base on Domestic Violence and Abuse (DVA) experiences of people with Multiple Sclerosis (MS) with a view to highlighting the implications for clinical practice. A...
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Application deadline: All year round Research theme: Systems and Control How to apply: uom.link/pgr-apply-2425 This 3.5 year PhD project is funded by The School of Engineering and is available
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Fixed-term: The funds for this post are available for 1 year. Applications are invited for a Research Associate (Postdoc) to join the Prorok Lab in the Department of Computer Science and Technology
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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of Engineering at the University of Birmingham and delivered in collaboration with industrial partners and the Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT) – one of five national hubs funded by
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sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The outputs of this work will support practitioners in making informed investment decisions