145 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Department of Political Science" research jobs at University of Oxford
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applications received before noon on Friday, 6th February 2026 can be considered. What We Offer As an employer, the University of Oxford is committed to the wellbeing and development of its staff. Benefits
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The Oxford Department of International Development is looking to recruit a Qualitative Researcher in British and European Politics who will report to Professor Masooda Bano, and will be a member of
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development opportunity, for which an initial appointment would be at grade 6 (£35,681 - £41,636 per annum) with the responsibilities adjusted accordingly. This would be discussed with applicants at interview
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, neurophysiology, and bioengineering. The position offers strong potential for scientific independence and career development, with opportunities to co-supervise students, present at international meetings, and
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diffraction experiments to examine strain and damage evolution. You will also collaborate in micromechanical testing and advanced characterisation (SEM/TEM/nano-XCT) to understand the mechanisms of deformation
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microwave circuit design and simulation, theoretical analysis of classical and quantum circuits, numerical method development, cleanroom device fabrication, cryogenic system operation, and microwave control
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advanced health economic methodologies. The successful candidate will be supported in their career development and encouraged to identify and pursue their own research interests, with a view to developing an
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during their PhD and in any postdoctoral positions. Securing a Fellowship will offer you the freedom to conduct your own research programme. To aid the development of their academic profile and to support
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the development of novel fluorination methods for the preparation of fluorinated molecules of strategic importance using readily available feedstock and cost-effective sources of fluoride including
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Regularization. We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical