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to create a sustainable future for aviation and power generation with a PhD in Future Propulsion and Power For general enquiries, please email Sara Horsfield ([email protected] ) Studentships
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Applications are invited for a Research Assistant or Research Associate to work on efficient machine-learning systems for earth observation. The post holder will be part of the Computer Architecture
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/clinical need proposed by our clinical collaborators. You will be working on genomic, molecular (transcriptomics, proteomic, and metabolomics), and electronic health record data, empowered by and
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candidate will have a PhD in a strongly quantitative discipline, but applications from candidates close to submitting their PhD are also welcome. Familiarity with deep learning methodologies is essential, as
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candidate will have a PhD in a strongly quantitative discipline, but applications from candidates close to submitting their PhD are also welcome. Familiarity with deep learning methodologies is essential, as
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, as well as contributing to service development. The ability to work well within a team and act as an efficient and pro-active service provider with good communication and presentation skills is
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addition, they should be able to demonstrate the ability to collaborate broadly within the field and support and mentor research students. Candidates should have or be close to completing a PhD degree in
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that utilise ion-pairing interactions. Radical reactions, in which electrons move singly rather than in pairs, have seen an immense pace of development in recent years. These exciting developments, many of which
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immunology/immunotherapy, immune regulation and inflammation biology. The ideal candidate will be motivated, independent and enthusiastic, and have a Ph.D. or MD/PhD* in immunology, cell biology, or a related
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. This proposed project will comprise the development and utilisation of advanced characterisation tools based on electron, X-ray and/or photon modalities to understand power losses and instabilities in next