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Field
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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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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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reducing waiting lists. This will be achieved through the following objectives: Acquire data and expert-based evidence and optimise data augmentation to ensure optimal hospital patient pathways through pre
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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Supervisory Team: Leonardo Aniello, Han Wu PhD Supervisor: Leonardo Aniello Project description: Blockchain and Federated Learning (FL) are two emerging technologies that, when combined, offer a
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A continual learning approach for robust robotic control in electric batteries assembly. This project is an exciting opportunity to undertake industrially linked research in partnership with
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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax