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through to large-scale individual-based simulation as well as statistics and Bayesian inference. This highly motivated, collaborative research group leads funded, international consortia in modelling, NTDs
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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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as soon as possible but must be available to start by 1 April 2026 at the latest. This project aims to develop superconducting microwave interconnects and metasurfaces for distributed quantum networks
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manifold learning and Riemannian optimisation to leverage the underlying manifold structure for better training and novel network designs. Low Effective-dimensional Learning Models. We will extend
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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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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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offer the greatest opportunity for energy independence and deep decarbonisation (i.e. process heat and hydrogen) to meet net zero targets. Our programme of research, collaboration, and skills development
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network of academic and practitioner partners. The SLP’s work focuses on four themes: (1) climate litigation; (2) legally relevant climate science (like attribution science); (3) climate policy (via
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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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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