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development. The ideal candidate will have a PhD in a relevant biological subject, together with experience in molecular biology, cell biology and immunology. Familiarity with flow cytometry, vascular biology
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development. The ideal candidate will have a PhD in a relevant biological subject, together with experience in molecular biology, cell biology and immunology. Familiarity with flow cytometry, vascular biology
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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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individual on an upward trajectory and will fully support applications for independent fellowships to follow this initial post. Applicants should hold a PhD (or equivalent) involving neuroimaging, cognitive
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desire to learn others: molecular biology, C. elegans or other model organism research, confocal imaging, computational analysis, preferably in python and electrophysiology. We are also looking for someone
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will
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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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to study health and disease. Accordingly, almost all of contemporary biological science research is critically dependent on our ability to identify which genes are related in different species. The Kelly
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and PhD students) and openly share expertise knowledge with team members (e.g. Post-docs). Collaborate with other two post-docs working in this research programme, to deliver on project aims. Contribute
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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