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. Designing deterministic and probabilistic forecasting models for wind power production and ramp events. Publishing scientific articles related to the research project and presenting results at international
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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. Experience in developing and applying advanced parametric/machine learning postprocessing techniques, producing probabilistic forecasts of hydrometeorological variables, and parallel computing. Proficiency in
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from animal studies to humans) in drug discovery, dynamical systems for long-horizon time series forecasting, and verifiably safe reinforcement learning. While both PhD positions are part of the same
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of probabilistic lake-to-lake routing and forecasting capabilities for operational partners (e.g., U.S. Army Corps of Engineers). Manage and analyze large hydrometeorological datasets from models, observations, and
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international research institution with high academic standards and an interdisciplinary work environment, developing forecasting systems and tools for conflict predictions? The Peace Research Institute Oslo
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for pattern extraction, explanation, and forecasting of the high-dimensional, non-stationary, temporal data encountered in energy finance. We design this new family of ML/AI instruments to provide distinct
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around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from weather forecasting to cardiovascular medicine
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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