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high-energy physics. The CERN experiments have been pioneering developments of ultra-fast deep learning algorithms for online monitoring and anomaly detection over the last decade. The PhD candidate will
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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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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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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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10th October 2025 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Machine Learning & Signal Processing for Industrial Applications Apply
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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
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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processing, time series analysis or machine learning for the interpretation of structural data is desirable. Basic knowledge of numerical analysis and design of structures for special load cases (earthquakes