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modeling into modern causal inference by combining its strengths with innovations in debiased machine learning, as well as to improve both the statistical efficiency and robustness of debiased machine
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programming. You are highly motivated to conduct (applied) research at the intersection of (deep) machine learning and the health sciences. You have good programming skills in languages such as Pythorch, and
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are looking for a m/f/x Doctoral fellow YOUR JOB You conduct doctoral research in the area of Augmenting Learning Environments Using Generative AI and Neuroadaptive Systems, with the aim to obtain a PhD after
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for focal epilepsy with ultrasound neurorecording, modulation, and deep reinforcement learning (DRL) closed-loop control. The technology will be developed through detailed computer simulations and preclinical
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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
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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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at Ghent University from September 2025. The project aims to develop a minimally to non-invasive treatment for focal epilepsy with ultrasound neurorecording, modulation, and deep reinforcement learning
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-of-the-art in machine learning, (probabilistic) modelling, system identification and numerical optimization. How to apply Send your CV, containing one or more references, a copy of your diploma (if already in
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. Digital Extraction from Historical Taxonomic Literature Application of OCR and machine learning algorithms to digitize printed and handwritten documents; Linking specimen mentions in literature to digital