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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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, antennas, and electromagnetic metasurfaces. The computer-aided simulation of electromagnetic fields is critical in the design of most computing and communications devices, such as high-speed interconnects in
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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delivered in routine practice for people with alcohol and drug dependence. This will be a large-scale longitudinal cohort study using national registry data on employment and health. A target trial emulation
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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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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brings together expertise in health data science, microbial genomics, and cancer bioinformatics. Th selected student will work under the supervision of Dr Arron Lacey, a specialist in machine learning and
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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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