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their application clearly which they are familiar with and their degree of expertise). Experience of geo-coded data analyses and Propensity Score Matching would also be relevant. Expert interviews will also form part
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of the project, working in an autonomous and highly organised manner, and will have excellent data management and analysis skills. In addition, you will be skilled at working effectively within multi
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, analytical and pilot brewing facilities supporting a research community of Postgraduate and Post-doctoral scientists and delivery of a range of specialist Brewing courses. The post is offered on a full time
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to explore own research interests. We are looking for a researcher with experience of data-intensive projects involving simulations or observations, good knowledge of galaxy formation physics, and the ability
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developing ideas for application of research outcomes. This post also be linked to research activities linked to the Faculty’s research platforms such as the Power Electronics, Machines and Control Research
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responsible for data/image acquisition, analysis and interpretation and for using this information to design efficient heterogeneous photocatalytic processes. This will involve working on the synthesis and
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of Nottingham, and with the core team at Queen Mary University as well as national partners. The post holder will be enthusiastically committed to conducting research that makes an impact and a team player. You
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We are looking for an experienced researcher with expertise in the generation and analysis of quantitative education data to work in the Observatory for Mathematical Education ; a collaboration
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colleagues to develop the research design undertaking the literature review liaison with project stakeholders data collection and analysis contributing to reports and outputs research project administration
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of component representative specimens for the generation of model validation data using full field visualization methods. - Manufacturing process simulation, particularly for welding and additive