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Field
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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cells survive treatment and cause the tumor to relapse. Furthermore, these therapies lead to long-term problems for children, including difficulties with memory, learning, and daily activities. Hence
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the upcoming flood type, e.g. heavy-rainfall flood or rain-on-snow flood. As PhD candidate you will compare several machine-learning based algorithms regarding their ability to predict the flood type based
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analysis, has good software skills (Python, C++, ROOT) and has (some) research experience in experimental particle physics. Experience with machine learning algorithms and software is desirable but not
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. Until now, specific EN fingerprints of localized corrosion are determined manually. This is a tedious procedure that requires considerable expert knowledge. Artificial intelligence or machine learning (AI
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computational neuroscience, connectomics and/or Machine Learning is appreciated. Our goal perform connectome analysis on neuroimaging data\integrate connectome findings across multiple speices work on developing
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to support coordinated decision-making for sustainable strategies in the port call? As a PhD student at TU Delft, you will leverage AI (i.e. optimization and machine learning techniques) to prepare ports
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motivated to do PhD research in mathematical machine learning, with special emphasis on mathematical foundations for explainable AI? If yes, the Korteweg-de Vries Institute for Mathematics at the University
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of the) [map ] Subject Area: Mathematical Machine Learning Appl Deadline: 2025/10/13 11:59PM (posted 2025/09/16, listed until 2025/10/13) Position Description: Apply Position Description Join Us! Are you
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these sounds fascinating, then this PhD position is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning