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interdisciplinary research and training environment with specialized seminar series and tech talks and training courses that develop both scientific and complementary skills. As a 4R-RTG PhD student, you will be
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part of a vibrant interdisciplinary research and training environment with specialized seminar series and tech talks and training courses that develop both scientific and complementary skills. As a 4R
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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
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processing, time series analysis or machine learning for the interpretation of structural data is desirable. Basic knowledge of numerical analysis and design of structures for special load cases (earthquakes
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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researchers associated with the Horne Family Charitable Trust program of research on DVA and MS. What we offer in return 26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom
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to work with international partners such as the WHO. The models developed will used to answer a series of substantial questions related to variation in exposure to air pollution over space and time
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to financial operations Develop systems for automated regulatory compliance checks, fraud detection, risk analysis, or smart contract orchestration Investigate the integration of agentic reasoning with financial
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Research Assistant in Classics and Archaeology About the Role The duties will include: Observing a series of training sessions, in-person at University of Nottingham. Assisting with the design of a
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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