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Forecasting (ECMWF) is focused on Copernicus Climate Change Service (C3S) and the Copernicus Atmosphere Monitoring Service (CAMS). The Research Fellow will play a key role in delivering UCC's contributions
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forecasting water consumption, based on data from smart meters and using statistical and machine-learning methods. The project will collect, structure and analyse consumption data, characterise usage patterns
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) (PDF) under the protocol established with +ATLANTIC - “Dynamic downscaling of European CAMS air quality forecasts to high resolution over Portugal”, CV 181/2024 of Centre for Environmental and Marine
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learning methods for analysing water consumption data, characterising consumption patterns, detecting anomalies and water losses, and forecasting demand, thereby contributing to the development
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Candidate (DC) to work on Work Package 3. As DC11, your research objectives are to: Harmonise genomic workflows and integrate AI for variant detection and outbreak forecasting Build secure, interoperable and
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generation algorithm based on different approaches to improve understanding the behavior of forecasting algorithms in time series and tabular data. The workplan will be as follows: Literature review Design of
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, attract global talent, catalyse external funding, and build long-run research leadership in areas where AI, markets, and institutions increasingly interact. For more details, please view https
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ecology, environmental forecasting and climate adaptation, fisheries and ecosystem-based management, ecological engineering and restoration, environmental sociology, and sustainability science. The Nature
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role The successful candidate will develop and evaluate AI-based spatio-temporal forecasting models for electricity demand at motorway EV charging hubs, integrating transport, charging, weather, and
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directly support nowcasting (for enhanced situational awareness), forecasting (to assess plausible outcomes in a business-as-usual scenario), the evaluation of control measures, and formal cost-effectiveness