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is involved in the DUNE project with a broad range of responsibilities in detector construction, modelling and software development. This PhD project will focus on the development of a methodology for
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supported by creative learning technologies. These two positions will contribute to the development and delivery of courses focusing on Quantum Computing, as well as teach theory-focused courses offered by
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algorithms using Monte Carlo simulation and Bayesian inference to distinguish normal tritium losses from suspicious discrepancies during transport, and to develop statistical thresholds that balance detection
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haptic guidance methods that respond to operator skill levels. Identify trajectory features that characterise expert performance for training robots. Develop algorithms that allow robots to refine
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treatment window may already have passed. Project Aim: This project proposes the development of an innovative approach that applies computer vision and machine learning to detect early signs of stroke through
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of war trauma through multi-generational oral histories. You will be working as part of an international team and specialising in the Francophone case study: Senegal. You will develop expertise on the case
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the Ice Sheet and Sea Level System Model (ISSM). and/or The development of automated (e.g., machine learning) approaches for mapping glacier-surface morphologies on Mars. You will join a cutting-edge
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, including teeth grinding and normal everyday movements, ensuring the accuracy and reliability of the collected data. Developing, training, and validating state-of-the-art machine learning algorithms
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demonstrated in several case studies in regenerative and climate-smart agriculture, aiming to promote regenerative practices while reducing greenhouse gas emissions. The case studies will be developed in
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interact with the world around us. However, the power requirements and carbon emissions of AI are equally dramatic: training a single state of the art algorithm has the same carbon footprint as the lifecycle