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Field
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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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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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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