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electrical power distribution system. Prior knowledge on power system condition monitoring would be an advantage. Experience in project work would an advantage. Share this job Facebook Twitter LinkedIn Apply
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infrastructure. We welcome applications from passionate, skilled, and committed individuals. About the Role The spatial distribution of schistosomiasis coincides with development of certain water management
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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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Experience with machine learning algorithms and ideally experience developing novel methods Understanding of basic biological principles and experience interpreting ‘omics data Ability to analyse information
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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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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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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
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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