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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing can be
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combined with human support. About the role The Research Scientist in Machine Learning for Wearables will develop predictive deep learning models to assess maternal and partner health and behaviour
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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predictive machine learning models for track and vehicle degradation using cutting-edge deep machine learning, and will integrate these into MoniRail's real-time monitoring system to deliver intelligent, data
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KTP Associate in Machine Learning ( Job Number: 25000811) Department of Computer Science Grade 7: - £39,105 - £43,878 per annum Fixed Term - Full Time Contract Duration: 30 months Contracted Hours
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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. We are now looking for: Three (3) Doctoral Researchers (PhD students) in Machine-Learning-Driven Atomistic Simulations The Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro