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Field
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ML methods for postprocessing numerical ensemble weather forecasts over India to improve the skill of precipitation predictions and to generate hydrological forecasts. Role Summary Implement and test
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Characterisation Apply and refine ML techniques for in silico annotation, prediction of physicochemical properties, and prioritisation of chemicals by toxicity or biological relevance. Integrative Analysis of Large
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description] Topic 1: Multi-model ensemble prediction of weather, sub-seasonal to seasonal climate variability (one position). Constructing a seamless atmospheric forecasting system with a large number of
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and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform the Intergovernmental Panel on Climate Change
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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position will contribute directly to ongoing developments at CW3E in the domain of AI weather modeling and prediction, including novel architecture and ensemble design. May also develop innovative deep
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) in weather forecasting and hydrological prediction. The Research Fellow will develop ML methods for postprocessing numerical ensemble weather forecasts over India to improve the skill of precipitation
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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform
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weather prediction. We are seeking a scientist motivated by real-world applications to evaluate the importance of data collected by instruments deployed from the stratospheric platforms developed by
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) Developing strategies for decadal predictions of the Baltic Sea climate Analysing the skill of decadal predictions Analysing large ensembles of multi-decadal scenario simulations Investigating natural climate