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Field
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together with industrial partners? Do you like supervising students and participating in teaching? If you are a creative and analytical person and have a PhD in a field related to mathematical modeling and
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opportunities to collaborate, take part in network events and undertake research placements within this project. You will have a PhD in Physical Sciences or a closely related field, and you will have a background
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network events and undertake research placements within this project. You will have a PhD in Physical Sciences or a closely related field, and you will have a background in the modelling of molecular
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validation, which will be led in collaboration with our partners. Essentials include a PhD (or near completion) in mechanical/chemical/thermal sciences or related field; hands-on experience in sorption
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at meetings, and present the team’s work to others. About you You will hold, or be close to completion, of a PhD/DPhil, together with relevant experience. You should have knowledge and practical experience in
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become an embedded member of both teams, joining a Senior Research Fellow and PhD student who engage in cross-technique research in a clinical setting. The hyperpolarised groups are strategically placed
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environment and impact with the QR funding allocated for the Faculty of Arts, Business and Social Sciences (FABSS). The plan is to support 0.6fte post-doctoral research fellowships under this scheme. With a PhD
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open to considering flexible working patterns. Your skills and attributes for success: A PhD (or to be obtained within 3 months) in a relevant area Expertise in expression and purification of recombinant
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to completion of) a PhD/DPhil in geotechnical engineering, along with experience in numerical methods, including the implementation of soil constitutive models in finite element code. You should have excellent
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, ensuring they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (e.g. Statistics, Machine Learning