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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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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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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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individually, make a real difference. The role We are looking for a part-time research assistant (1 day a week for a year) to work with us on a project that aims to develop a set of resources that support
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. Eligibility To enrol in January 2026 (applications will also be considered for deferred entry in September 2026). To enrol in Full-Time study on the Research Degree Programme. Available for Home or
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: Diagnosis, RehabilitatiOn & Prognosis). The Doctoral Researcher (DR) will join a cohort of DRs who will be working on a series of interlinked, interdisciplinary projects for sustainable, intelligent, and
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. This project will investigate how such oscillations can be mitigated using series connected flexible AC transmission (FACTS) devices. The project will be carried out in close collaboration with GE Vernova
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: Diagnosis, RehabilitatiOn & Prognosis). The Doctoral Researcher (DR) will join a cohort of DRs who will be working on a series of interlinked, interdisciplinary projects for sustainable, intelligent, and
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nature-based solution opposed to traditional ‘grey’ engineering, offer catchment-level solutions by using natural processes to slow and store water through a series of diffused interventions. Historically