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acquired and will also be determined by the funding available. About you You should hold a PhD/DPhil, or be close to completion, of a relevant PhD/DPhil with a proven record of being able to learn and apply
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small highly motivated inter-disciplinary team working towards a shared goal. You will be responsible for the design and testing of original machine-learning based algorithms and models for multi-modal
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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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projects and publications from the group can be found here: https://www.kcl.ac.uk/research/pavri-group About the role The project is focused on combining artificial intelligence (AI)-based machine learning
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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach
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superconducting qubits and millikelvin electronics Did you recently get your PhD in circuit quantum electrodynamics (cQED) and are now looking into taking the full potential of your skills into use for making new
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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researchers and PhD students, pursuing research in many aspects of field theory, string theory, and integrability (for more information, see www.hw.ac.uk/uk/schools/mathematical-computer-sciences/research/maths
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for a researcher interested in parasite biology for a wet-lab based role. Applicants should have (or be close to completing) a PhD in microbiology, biochemistry, molecular cell biology or a related field
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will be educated to PhD level with relevant experience in molecular plant biology and evolution and will work closely with other group members to assist them with gene functional characterisation