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and most comprehensive dataset on multiple sclerosis (MS), encompassing longitudinal data from over 40,000 individuals, some tracked for more than a decade. You will be responsible for advancing and
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individuals, some tracked for more than a decade. You will be responsible for advancing and applying state-of-the-art probabilistic deep generative models, including conditional diffusion and flow matching
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the frequency domain, photoelectron spectroscopy will offer insight into the dynamics of the resonance from a static perspective, while time-resolved photoelectron spectroscopy will track the autodetachment
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spectroscopy will track the autodetachment to internal conversion and dissociative electron attachment products in real-time. Key responsibilities: To understand and convey material of a specialist or highly
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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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) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting. Previous experience and
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), and sustainable chemistry. In particular, the role will involve: (i) designing blockchain architectures and smart contracts for CO2 credit tracking, tokenisation of CO2 derived products; (ii
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at the intersection of blockchain, artificial intelligence (AI), and sustainable chemistry. In particular, the role will involve: (i) designing blockchain architectures and smart contracts for CO2 credit tracking
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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systems onto real robots for tasks such as tracking, 3D reconstruction, object recognition, and visual SLAM. They will be working with a team composed of PhD students, Research Assistants, and Postdocs