9 complex-network Postdoctoral positions at University of Cambridge in United Kingdom
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networks. This position is part of a UK-Canada Quantum for Science collaborative project "Quantum network applications in theory and practice" funded by STFC/EPSRC (UK) and NSERC (Canada), led by Professor
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coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self-motivation, research
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(PPA) for inclusion in our simulator framework. Candidates will ideally have worked on complex digital hardware designs and be familiar with standard EDA design tools from Cadence and Synopsys
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well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self
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, the dissemination of research results, and the support of others. We have an open mind as to the skills profile of a successful candidate for this advert. Experience in leading methodological or complex applied
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interdisciplinary collaboration. The candidate will have the opportunity develop a large network of research collaborators based at universities and non-governmental organizations. The work is on site. There will be
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molecular and computational approaches. They will hold a PhD in a relevant subject, have a solid computational background, and be able to curate, analyse and interpret complex single-cell data sets
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at the Department of Chemistry at the University of Cambridge (https://www.ch.cam.ac.uk/group/michaelides ). The projects will involve the development and application of approaches for treating complex intermolecular
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application of this understanding to reprogram root developmental plasticity. We also collaborate with mathematicians to build multi-scale understanding of these complex and important signaling pathways