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Research Fellow (Fusion Shielding Materials) - School of Metallurgy and Materials - 105638 - Grade 7
limited to testing fusion materials and their joints to the effect of irradiation, high temperature, heat flux/plasmas, quantify thermo-mechanical property degradation and map failure mechanisms to guide
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programme investigating early lung fibrosis at King’s College London. It is anticipated candidates will have a relevant PhD in immunology or respiratory sciences, and have experience with cell culture
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computational social science and computational mental health to capture fine-grained behavioral data, uncover underlying mechanisms, and design interventions that reduce harm, promote healthier information
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to discuss the post informally, please contact Dr Yuanchang Liu () About you The ideal candidate will hold a PhD (or be close to completion) in Mechanical Engineering, Robotics, Marine Engineering, or a
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fields: architecture; building science; mechanical engineering. Possessing a Master’s or PhD degree will be advantageous with prior experience working in the architecture and/or sustainable building design
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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Vaccine Network, a UK Aid programme to develop vaccines for diseases with epidemic potential in LMICs. Additional co-funding from the EPSRC will leverage impact for the UK. VaxHub Global’s vision is to
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August 2025 Background To create and contribute to the creation of knowledge by undertaking advanced analysis and validation experiments within the MRC BioFlare research programme: Although outcomes in RA
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Applications are invited for a Research Assistant in chemical engineering and mechanical engineering with a particular emphasis on chemical energy material characterisation . The research project
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading