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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
parameter identification; Implementation of simulation, forecasting, AI-accelerated computational models, and digital twins; Support for the validation and technological maturation (TRL) of project results
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economic policy how firm- and household-level data can contribute to our understanding of business cycles, forecasting, monetary policy, migration and corruption other policy-relevant questions in applied
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into visions and scenarios for the energy transition Mapping/visualising Indigenous knowledge of climate and weather to support energy forecasting and planning More broadly, applicants are encouraged
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increasingly rely on scenarios, models, forecasts, roadmaps, and other forms of anticipatory knowledge when making decisions about energy systems, infrastructure, industrial development, and technological change
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of Sciences (UCAS), integrated into the MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation led by Professor Ying Liu. There is an active group of PhD students and postdocs working
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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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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term