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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 design and implementation of actuation and sensing technologies, ideally in medical or radiological applications • Proven track-record of high-quality research • Experience of robotics
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of the broader research objectives Contribute intellectually to the direction of the project by identifying opportunities to innovate, troubleshoot experimental challenges, and refine research questions Prepare
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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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. The study will involve computational modelling of dynamic aperture and coherent instabilities based on single- and multi-particle tracking simulations, as well as designing and conducting experiments
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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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identification of project objectives from assessment of the literature, the design/analysis of experiments, and the drafting of scientific publications. E7. Established publication track record in a relevant field
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-motivated in designing and executing experiments, with a proven track record in most of the following techniques: cell culture, animal work, flow cytometry, molecular cloning, confocal microscopy and