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challenges in the area of hazard assessment and impact forecasting. The aim of the project is to develop methodologies for forecasting future energy use for various assets and weather scenarios from short term
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learning models to forecast and visualize heat flow and occupant comfort metrics. Identify key drivers of energy inefficiency, including occupant behaviour patterns and physical heat loss hotspots. Formulate
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and work on multi-commodity energy districts? The envisioned research is part of the research program Intelligent Energy Systems (IES) performed within the Electrical Energy Systems (EES) group of TU/e
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deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting, system optimization, flexibility management, and resilience analysis. The work will be
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and work on flexibility estimation from industrial sites? The envisioned research is part of the research program Intelligent Energy Systems (IES) performed within the Electrical Energy Systems (EES
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the genesis of gravitational crises. These crises result in hundreds of landslides in a matter of days, as in January 2018, when more than 150 events were recorded in 48 hours. Conventional forecasting models
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value chains. The objective of this PhD project is to develop AI enabled route planning and navigational intelligence for risk based and energy efficient maritime navigation. The work will use
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for IoT communication and sensing applications with orders of magnitude less energy consumption than current solutions. Leverage large bandwidth at mm-wave to deploy several mm-wave IoT devices in dense
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data from hydrodynamic labs for validation purposes. Apply the AI models to develop an AI-based decision support framework for operability assessment for ships in planned voyages under forecasted
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, climate solutions and energy efficiency. Learn more about our institute at https://www.sdu.dk/en/mmmi and the centre at https://www.sdu.dk/cis . The Centre is autonomous but will collaborate closely with