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of career. Experience in using qualitative data analysis computer software such as NVivo would be an advantage. Skills 13. Demonstrable ability to plan and manage research, both independently and as part of a
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. This role offers a rare opportunity to contribute to cancer interception science at the frontier of personalised prevention, with strong translational potential. We are seeking a talented and motivated
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on Experimental Study and Computational Solids Mechanics model of soft multifunctional materials such as magneto-active polymers (MAPs), Electro-active polymers (EAPs). The successful candidate will work on the
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candidate with skills and experience in some of the following areas: Quantitative approaches such as longitudinal data analysis, analysis of large datasets and/or data science Experience of working with
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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track
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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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for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria 1. PhD degree in Engineering, Computer Science
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and social data with the aim to better understand energy use. The programme is an interdisciplinary collaboration between the School of Geography and the Environment (Oxford), the Department
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experience in Oxford Nanopore Technologies (ONT) sequencing and bioinformatics A track record of research in microbiome science, metagenomics, whole genome sequencing, big data analysis, machine learning, and
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postdoctoral researchers and PhD students, pursuing research in many aspects of field theory, string theory, and integrability (for more information, see https://www.hw.ac.uk/uk/schools/mathematical-computer