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probabilistic forecasting models, including uncertainty quantification. Automation of forecasting processes, covering data preparation, model training and updating, forecast generation, performance monitoring
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variablemodels), efficient inference methods(e.g. sampling methods), uncertainty quantification, calibration, and probabilistic forecasting, as well as the interdisciplinary application of these methods
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evaluation framework covering statistical fidelity, temporal and spatial structure, physical plausibility and downstream task performance (e.g. train-synthetic-test-real forecasting). You will collaborate with
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. In this way, flood impacts can also be described probabilistically and later added to the model of Pillar 1; Translating the current scenario sets into future climate states (using climate scenario as
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, forecast, and crowd-sourced station data into probabilistic maps. The challenge: conditioning generative models on large, heterogeneous, incomplete data will require the student to work on cutting-edge ML
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), ISCTE. Organic Unit: Centre for Computational and Stochastic Mathematics Scholarship Theme: A Forecasting Framework for Pandemic Monitoring and Decision-Making Duration: 6 months Maximum Duration
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
-informed/physics-guided models to complex engineering systems Development and use of advanced scientific AI methodologies, including surrogate models, generative models, probabilistic models, and both data
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knowledge of time series analysis and forecasting, with knowledge of multivariate, hierarchical and/or probabilistic forecasting Skills Essential: C1 Knowledge of time series analysis and forecasting C2
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
methodologies to complex engineering systems; Development and application of advanced Scientific AI methodologies, including surrogate models, generative models, probabilistic models, and data-driven and physics
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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