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of hazard assessment. North- Atlantic cyclones can cause severe damage to the neighbouring land of North America and Europe. Increasing the lead time of seasonal forecasts will allow for governments
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challenges in the area of hazard assessment and impact forecasting. The aim of the project is to develop methodologies for forecasting future energy use for various assets and weather scenarios from short term
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About the Project Applications are invited for a PhD position in “Probabilistic Artificial Intelligence” at the School of Mathematics, University of Bristol. Please visit the page below to see
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offers a fully funded, four-year PhD studentship which will start in October 2025 on one of the following themes: Theme 1: AI-Driven Solar Forecasting for Smarter PV Integration Theme 2: Next-Generation
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offers a fully funded, four-year PhD studentship which will start in October 2025 on one of the following themes: Theme 1: AI-Driven Solar Forecasting for Smarter PV Integration Theme 2: Next-Generation
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, and probabilistic methods are encouraged to apply. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £20,780 for 3.5 years full-time. The student will be based
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related discipline. Strong background/skills on machine learning, mathematics, probabilistic modelling and optimisation are preferred. To apply please contact the supervisor, Dr Mu - Tingting.Mu
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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
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develop probabilistic guarantees that quantify uncertainty in human preference alignment while ensuring robustness against adversarial conditions. The ability to mathematically verify AI alignment has
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process, and this process itself can impede certain policy. This project involves summarising models of political choice (e.g. the median voter, probabilistic voting, citizen candidate, etc models) with a