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modelling techniques and the embedding of such models within data assimilation frameworks to enable their self-correction. During this project, you will develop expertise in machine learning-based reduced
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professional development workshops in areas such as research communication, computing and data science, and professional progression through our Early Career Researcher Institute .Additionally, you will be
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conferences. You will have access to engaging professional development workshops in areas such as research communication, computing and data science, and professional progression through our Early Career
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: Please use reference number AE0083 Research Supervisor: Emile S Greenhalgh Research Group: Aero For further information: For questions about the project, email: Emile S Greenhalgh, e.greenhalgh
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London in this area, go to: https://www.imperial.ac.uk/mechanical-engineering/research/ For information on how to apply, go to: http://www.imperial.ac.uk/mechanical-engineering/study/phd/how-to-apply
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, go to: https://www.imperial.ac.uk/mechanical-engineering/research/ For information on how to apply, go to: http://www.imperial.ac.uk/mechanical-engineering/study/phd/how-to-apply/ For further details
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, please contact Dr Jun Jiang at jun.jiang@imperial.ac.uk . Further information on research in mechanical engineering at Imperial College London can be found at: https://www.imperial.ac.uk/mechanical
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and quality‑controlled, generating high‑quality serological, molecular and genomic data alongside metadata that will feed dashboards as well as catalytic and phylodynamic models. The post includes
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mix is changing, with a growing share of clean energy sources, mostly variable renewable energy sources and nuclear. Second, electricity demand is rising due to the expansion of data centers, electric
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for hardware control and data visualization. • Experience in working in wet-lab environment and/or dealing with food samples • Industry experience in product development and/or in vitro diagnostics is highly