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. Experience conducting systematic literature reviews or meta-analyses to inform model assumptions would be advantageous. We are seeking a self-motivated researcher with a PhD (or near completion) in Building
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-world interventions. A key part of this role will be performing systematic literature searches and meta-analyses of dose–response and exposure–response data to support model calibration and uncertainty
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searches and meta-analyses of dose–response and exposure–response data to support model calibration and uncertainty analysis. This role offers a rare opportunity to bridge public health and building science
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clinical trials within the unit. You will also be able explore a wide range of research skills including human volunteer and clinical stroke studies, data analysis including systematic review and meta
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studies, data analysis including systematic review and meta-analysis, scientific writing (abstracts and academic papers) and preparing grant applications for future studies. You will experience clinical
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
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computers”(under the UKRI Guarantee scheme). Topics include: - Quantum many-body dynamics - Quantum algorithms - Quantum-enhanced numerical methods - Quantum machine learning - Tensor Networks - Topological
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unique combined system using an optimised AF scanning procedure that integrates Raman measurements to analyse lymph node biopsies within 10 minutes and machine learning algorithms to deliver quantitative
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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 at the Nottingham Breast