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is a growing recognition of the importance of skills forecasting as a tool for a future-proof approach to labour market changes and challenges that affect businesses and citizens (see Mario Draghi’s
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, you will have the opportunity to advance scientific expertise on integrated modelling of the entire water cycle, with particular focus on real-time simulations and operational forecasting. A key element
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AI-Based Forecasting Models We invite applications for a PhD position focused on developing next-generation AI-based forecasting models to enhance renewable energy (RES) integration into power systems
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working for about fifteen years on the development of short-term forecasting systems designed to anticipate these phenomena by a few hours. Better anticipation should allow civil protection and emergency
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datasets and an evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting). You
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datasets and an evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting). You
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of granular heating and fragmentation, and feed the results into a new depth-averaged model of debris-avalanche dynamics. Your work will directly improve the physical models used to forecast the behaviour
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preferably experience with ecosystem modelling, ecological forecasting, phytoplankton ecophysiology and/or trait-based ecology. Please note that we do not expect that candidates master all techniques from
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operator with battery storage participating in the day-ahead market and the mFRR capacity and energy activation markets. The research will investigate how probabilistic forecasts, market-state and regime
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Cognitive architectures for self-reflective, self-improving AI Data-driven agents for reasoning, forecasting, and knowledge integration Safe, transparent, and privacy-preserving agent-based AI Eligibility and