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through high-quality publications. Desirable Criteria: 13. A PhD (or be close to submission) in Electrical Engineering, Energy Systems, Control Engineering, Computer Science, Artificial Intelligence, or a
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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at conferences and seminars. The successful candidate will be required to work in collaboration with other postdocs and PhD students in the lab. As such, the ability to work in a team is essential. This role will
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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Science Partnerships Fund Early Career Fellowships programme, delivered by the British Council. The International Science Partnerships Fund puts research and innovation at the heart of our international
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and science exploitation of the MIGHTEE survey data. The postholder would have the opportunity to identify new discoveries in the data and would be ideally placed to lead the science based on the data
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largest protein/peptide mimotope dataset foundational for advancing any peptide-based drug discovery programme. The ERC PoC PepVerse project focuses on increasing the technology readiness of this platform
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The Department of Earth Sciences, University of Oxford, seeks to appoint a full-time Postdoctoral Research Assistant in Active Faulting and Earthquake Hazards. The post holder will report to Prof
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fostering multidisciplinary research across a range of scientific domains. The Centre for Innovation in Genomics and Microbiome Sciences (CIGMiS) undertakes research aimed at addressing complex, systems-level
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and science exploitation of the MIGHTEE survey data. The postholder would have the opportunity to identify new discoveries in the data and would be ideally placed to lead the science based on the data