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biomass remote sensing, crop modeling, data assimilation and machine learning Supervise master thesis students For PhD students: follow training in line with the doctoral school requirements Where to apply
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combine EMI footprints, which capture normal variations through characteristic curves and statistical distributions, with state-of-the-art machine learning and deep learning techniques (e.g., one-class
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the computational dosimetry framework for interventional procedures such that it can be implemented in hospitals. With current artificial intelligence (AI) technologies, and particularly machine learning (ML
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support tasks for the Bittremieux Lab, such as assisting in practical teaching sessions and supervising Bachelor and Master students. Profile You hold a Master degree in Computer Science, Machine Learning
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, mathematics or a related domain. You have a solid academic track record, at least at the cum laude level. You are interested in both Machine Learning and Symbolic/Logic-based AI methods. You strive
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-type specific samples, state-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches
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have some knowledge and/or experience in several of the following topics (ordered by importance): Wireless Communication Technologies Distributed and Embedded Systems Machine Learning Data Analytics and
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machine learning, and exploring causal inference in complex biological systems. As a member of our team, the successful candidate will have the opportunity to work closely with experts in the field and
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knowledge and/or experience in several of the following topics: Optimisation algorithms Machine learning algorithms Swarm intelligence Algorithmics Parallel/Distributed computing Space systems engineering
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discipline The ideal candidate should have some knowledge and/or experience in several of the following topics: Optimisation algorithms Machine learning algorithms Algorithmics Smart buildings Internet