510 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"BioData" PhD positions in United Kingdom
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undertake an interdisciplinary programme combining polymer synthesis, materials characterisation, rheology and mechanical testing, and in vitro drug release studies, supported by quantitative analysis and
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Location: Mechanical and Aerospace Systems Research Group, Faculty of Engineering, University of Nottingham Funding: UK Home fees + tax-free stipend of £24,000 p.a. for 4 years Applications
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Linguistics and English Language Planning, Property and Environmental Management Politics Russian and East European Studies Science, Technology and Innovation Policy Social Anthropology Social Network Analysis
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PhD Project: Advanced 3D-Printing of Responsive Biomaterial Devices Applications are invited for a PhD project within the University of Nottingham’s Faculty of Engineering, in the Centre
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subject – Biomedical Sciences, Biomedical/Information Engineering, Computer Science, Analytical Bioscience, Physics or related disciplines. Prior experience with medical imaging, particularly MRI, medical
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degree with honours (or equivalent) in Engineering, Physics, Mathematics, or Materials Science • Excellent English written and spoken communication skills • Foundations in Continuum Mechanics and
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). The WIRe programme offers a bespoke training programme in technical and personal skills, and access to world-leading experimental facilities. The successful candidate will also have the opportunity
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(Centre for Doctoral Training in Modelling of Heterogeneous Systems) at the University of Warwick is an innovative, interdisciplinary fully funded PhD programme that brings together science, engineering
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mycoprotein ABUNDA. This placement will provide hands-on experience of large-scale mycoprotein production linking fundamental biological research with industrial innovation and pre-market food technology. Using
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap