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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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and near infrared, mid infrared and advanced machine learning and artificial intelligent modelling to enable accurate monitoring of nitrogen mineralization rate to enable understanding and improving
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techniques) at UGent combined with machine learning, deep learning and data fusion modelling to enable development of novel decision support systems for variable rate fertilization and manure application. He
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your knowledge and skills on state-of-the-art in machine learning, (probabilistic) modelling, system identification and numerical optimization. How to apply Send your CV containing one or more references