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enquiries may be addressed to Prof Craig Vear at craig.vear@nottingham.ac.uk . Please note that applications sent directly to this email address will not be accepted. Our university is a supportive
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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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affordability, welfare, and income distribution. Contribute to the preparation of policy briefings, academic publications, and public-facing reports. Present findings in academic and policy settings, including
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welcome applications from passionate, skilled, and committed individuals. About the Role The spatial distribution of schistosomiasis coincides with development of certain water management infrastructure
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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. Develop research objectives and proposals for own or joint research, in the area of interest of the hosting laboratory (Prof. T. Carlomagno) Contribute to writing bids for research funding Acquire, analyse
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behind this efficiency by developing a general model of wing mechanosensing, revealing how sensor distribution and morphology have co-evolved with flight dynamics. The successful applicant will: Measure
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project. Role Summary We are seeking to recruit a Research Fellow at the School of Dentistry, University of Birmingham, with state of the art research facilities, working with Prof. Gabriel Landini and Dr
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of schistosomiasis across rural-to-urban settings and develop tools to support targeted interventions. A key focus will be on mapping snail vector distribution near expanding water infrastructure (e.g., sand dams) in
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre