67 postdoc-in-thermal-network-of-the-physical-building PhD research jobs in United Kingdom
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interested in building clinical datasets to tackle translational challenges, while further developing neuroimaging and machine learning skills, and offers a unique opportunity for the appointee to join a
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an unprecedented opportunity to further our understanding of genetic epilepsies and to create robust, clinical useful imaging tools. The post would suit a candidate interested in building clinical datasets to tackle
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with the Department of Statistics at University of Warwick, Department of Mathematical Sciences at Durham University, and School of Mathematics, Statistics & Physics at Newcastle University. You will
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About the Role The Nuffield Department of Clinical Medicine is seeking to appoint a Researcher in Genomics of Infections to join Dr Azim Ansari’s group, based in the Peter Medawar Building
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both homogenous and heterogenous catalytic conversion, including the handling, testing and characterizing the catalysts as well as the reactor and process simulation and analysis. Details will be
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contribute to academic papers. Where required, public involvement (PI) activities Make independent decisions to ensure timely and effective completion of task Ideally the candidate will have a strong
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18 months Fixed Term to start December 2025, Full Time Monday to Friday We are seeking a motivated and experienced Research Assistant to join an innovative, co-produced initiative aiming to make
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of CLR, departmental, and University activities, including seminars, teaching, and interactions with a range of research groups. The successful candidate will make a key contribution to the assessment
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involvement (PI) activities Make independent decisions to ensure timely and effective completion of task Ideally the candidate will have a strong understanding of the language, cultural norms, and lived
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems