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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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Agency (ARIA). The PROTECT project (Probabilistic Forecasting of Climate Tipping Points) brings together cutting-edge AI, statistical, and machine learning techniques with climate modelling, aiming
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encourage applications from candidates with experience in: • Edge AI system development or validation • Probabilistic forecasting and clustering • Cybersecurity for distributed systems
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are making landfall. Evaluate and improve the skill of probabilistic forecasts of compound air-sea extremes and identify key sources of uncertainty in the Southeast Asia region (Year 2). Understand
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. Develop and implement AI-based probabilistic forecasting systems, particularly targeting improvements in subseasonal-to-seasonal (S2S) weather prediction. Ensure that AI models accurately represent extreme
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, coordinates and manages meteorology- and verification-related projects and tasks under the California Atmospheric Rivers Program and Forecast Informed Reservoir Operations Program at CW3E in collaboration with
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forecasting. The team behind HiGrav includes researchers from ETH Zurich (lead), University of Bern, GFZ Helmholtz Centre for Geosciences and TU Braunschweig. The project aims to improve flood forecasts using
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simulations, and developing methods for deterministic and probabilistic predictions of hydrometeorological variables. Knowledge of operational forecasting models and products from NOAA and ECMWF. Knowledge
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and temporal resolutions have significantly transformed the way we approach climate and weather forecasting. These technological advancements have paved the way for innovations, such as the integration