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centres on natural science disciplines, particularly meteorology, emerging sensor technology, citizen science, and the development of early warning systems for location-specific natural hazards
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collaboration in optimising data analysis algorithms for the raw detector data, including improving position reconstruction and pulse-shape discrimination algorithms which can be implemented on in the front-end
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will be part of the project group ''Cancer Biology in Silico '' led by Chloé B. Steen, with strong collaborations with researchers at Oslo University Hospital, University of Oslo, and Stanford University
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine