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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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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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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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) 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
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to transform our understanding and prediction of climate tipping points. We welcome candidates with expertise in climate modelling, ideally including experience with General Circulation Models (GCMs), Earth
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | about 1 month ago
of multiple timescales. Collectively induced stochastic resonance phenomena on molecular ensembles in optical cavities. Cavity-induced off-equilibrium consequences on chemical reaction rates Develop (ab-initio
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. Moreover, the successful candidate will also need to develop a system to estimate the uncertainty of the predictions. Potential solutions could include ensemble generation, a combination of EOF