11 phd-computer-network Postdoctoral positions at University of London in United Kingdom
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at scale. About You Applicants should preferably have an MSc/PhD in Computer Science/Engineering. They should have expertise in distributed systems and computer networks. Excellent knowledge and practical
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researchers at Queen Mary University of London (Dr Margherita Malanchini) and King’s College London (Professor Robert Plomin), contributing to a broader network of PhD students and postdoctoral researchers. Key
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About the Role This Postdoctoral Research Associate (PDRA) position is part of an exciting EPSRC-funded programme, "Enabling Net Zero and the AI Revolution with Ultra-Low Energy 2D Materials and
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of computational and behavioural neuroscience with modelling and domestic chicks’ data. This position is funded by a Leverhulme Trust project entitled “Generalisation from limited experience: how to solve
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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collaborative and interdisciplinary and the ability to work in a team is essential. About You The successful candidate will be expected to have a PhD degree in biological or computational sciences or equivalent
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have a PhD and track record in either computer science with specialisation in relevant AI technologies for surrogate modelling, or in Earth or Environmental Science with a strong track record in
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to refereed journals, and work with and help supervise BSc, MSci, and PhD students. The successful applicant will have a PhD in Astrophysics, Theoretical Physics, or a related discipline and prior experience
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-purposing. This role is a fantastic opportunity for someone passionate about leading innovative research and making impactful contributions to the field of computational biology. You will hold a PhD (or close
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Research Council’s (AHRC) Bridging Responsibilities AI Divides (BRAID) programme that will explore new technologies, new business models and new approaches to data provenance in pursuit of an equitable