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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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one month and 30% within one year after hip fracture surgery. It is therefore crucial to monitor patients’ health condition continuously and accurately after surgery to measure and evaluate patients
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development
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collaborative labs develop and deploy the latest technology, including sensing, data analytics, modelling, simulation, artificial intelligence, and machine learning, and function as dynamic hubs where innovative
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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smart monitoring methods can be used to investigate the ecological status of smaller -often unmonitored- water bodies. These water bodies make up one-third of the total number of water bodies in