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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
state-of-the-art wearable devices. Applicants should have strong experience in data collection in applied settings, advanced statistical analysis skills (particularly in time-series data), and proficiency
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the coupled effect on supersonic combustion. This data can then help support future design efforts, which requires validation data where the effects of thermal warpage can be included. To achieve
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are eligible to apply. The successful candidate will receive a tax free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. The start date is 1st October 2025. Visualising data
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drives tumour development, childhood cancers lack the extended time frame needed to accumulate the mutations required for tumorigenesis by those routes. Therefore, endogenous mutagenic processes are a
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gestural interaction for music. Proposals should outline a research project that uses time-series classification to support the detection of musical gestures and interactions with different materials and
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, Skills and Experience • You will have substantial technical experience in time series analysis, ideally either in neurophysiology data or wearable sensor data • You will have experience of at least
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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or as soon as possible thereafter. For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £20,780 for 3.5 years full-time, or pro rata for part-time
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political philosophy literature, drafting papers, developing and coordinating a seminar series, and overseeing the development of a journal special issue, and of a podcast on this cluster of topics. You will
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to air pollution in the future, and planning further policy changes. This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series