41 machine-learning-"https:"-"https:"-"https:" Postdoctoral positions at University of Oxford
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• An excellent contributory pension scheme • Childcare services • Family leave schemes • Cycle and electric car loan schemes • Employee Assistance Programme • Membership to social and sports clubs
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legitimacy, and the development of societies under the conditions of 'abusive constitutionalism' (Russia), open military conflict (Ukraine), deep transformations (Romania) and democratic backsliding (Hungary
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research experience and to learn from leading academics. The ideal candidate for this role will have a strong research background with experience of a variety of qualitative and quantitative methods, and a
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opportunity to gain academic research experience and to learn from leading academics. The ideal candidate for this role will have a strong research background with experience of a variety of quantitative and
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specialist laboratory equipment such as tissue culture hoods, PCR machines, microscopes and flow cytometers. You will maintain laboratory databases, analyse and interpret experimental data, and generate
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specialist knowledge in the discipline to work within established research programmes. You will also have experience writing computer code in Stata and/or R for data analysis and visualisation and have
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electronic and optical characterisation, and nobody arrives with all of it. You will enjoy learning, and take satisfaction in taking a polymer from synthetic design through to working material. You will be
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reliability of these materials in optoelectronic devices The post-holder will have the opportunity to teach. About You Applicants should possess or be close to obtaining a PhD in physics, materials science
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contributory pension scheme 38 days annual leave A comprehensive range of childcare services Family leave schemes Cycle and electric car loan schemes Employee Assistance Programme Membership to a variety of
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About the role The postdoctoral researcher will work on a project funded by Amazon Robotics that aims to improve decision-making in large mobile-robot fleets through learning-based corrections