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-time reinforcement learning algorithms to personalise training difficulty for the DRUM-AI training app and help shape the future of AI-assisted rehabilitation. The post holder will be based in
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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meaning from data. Designing machine learning algorithms and undertaking testing for: predictive maintenance. operational performance and cost optimization. failure prevention and timely reparation of Aids
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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river system Develop, test and apply algorithms for the processing and analysis of satellite data drawing on the latest physics-based and/or data-driven techniques Contribute to work on the automation and
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Job Purpose To make a leading contribution to the development of innovative muon tracking algorithms for cutting-edge applied nuclear physics research. The successful candidate will be expected
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial