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an exceptional environment for career development in 2D materials and sustainable electronics. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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of Pathology, University of Oxford to study interplay between ADP-ribosylation and ubiquitination signalling in DNA damage and/or immunity responses. The post-holder will be responsible for managing own academic
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deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing
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of Engineering Science (Oxford) and University College London. The instrumentation for the Observatory is developed in dedicated Laboratories of 100- 200 homes. Novel and innovative instruments will be rigorously tried
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postdoctoral researchers in synthetic organic chemistry in the Department of Chemistry at the University of Cambridge. The successful candidate will join the dynamic and collaborative research group of Professor
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21 Aug 2025 Job Information Organisation/Company DURHAM UNIVERSITY Research Field Cultural studies History Geosciences Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
strong effects on guest binding, diffusion and even the dynamic behaviour of the entire MFM. In this project, funded by the Leverhulme Trust, we will develop a machine learning platform to describe
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of 24 months. The project aim’s to develop new constitutive models to describe the mechanical behaviour of Thermoplastic Elastomers (TPEs). These polymers are increasingly being developed as a
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within-country disparities in reproductive behaviour, with a focus on childbearing age and fertility levels. The project will develop a framework to understand global fertility dynamics and the links