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application of conditional diffusion models, flow matching techniques, or related generative approaches, as well as experience working with probabilistic (Bayesian) methods and statistical modelling. Strong
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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continuous seismic datasets from Costa Rica’s National Seismological Network (Red Sismológica Nacional; RSN) creating enhanced earthquake catalogues that will illuminate subsurface volcanic, tectonic and
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relevant to setting a roadmap for ongoing experiments, as well as recently developed applications of tensor network techniques to large-scale partial differential equations. We are advertising two positions
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to understand and predict how technologies evolve — from artificial intelligence to net-zero innovations in energy, transport, and carbon capture. By building a global database on technological progress, we seek
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reactions. The work will extend ChemZIP to handle catalytic chemistry, develop a reduced-order 1D network model linking reaction and pressure-drop effects, and use it to optimise catalytic structures within
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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out rigorous and impactful research into the computational mechanisms of human learning using deep neural network models, and disseminating the findings within the research group, across the wider
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The post holder will develop computational models of learning processes in cortical networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity
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comfortable budget to conduct both project-related activities and activities related to their independent career development; iv) benefiting from the team wide academic and not-academic network, including