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application of cutting-edge causal machine learning methods You will further elaborate and concretise the PhD theme and research tasks at the start of the PhD in consultation with the supervisor and any co
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), consists of two main parts. First, the candidate will develop machine learning models aimed at improving the follow-up of neurocognitive function in critically ill children after discharge from the intensive
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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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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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will be working in both groups (50% at UAntwerp, 50% at KU Leuven ) for the full duration of this project, who will be closely assisted by two postdoctoral fellows, based respectively at A-PECS (UAntwerp
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on: • In-depth expertise in reliability testing of wide bandgap (WBG) technologies • Deep knowledge in in-situ measurement techniques for WBG technologies • You work on developing hybrid prognostic
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Intelligence (AI). Despite recent advances in data-based AI (e.g., deep learning and LLMs), knowledge-based methods are still state-of-the-art when it comes to building reliable and explainable decision support
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This fully funded PhD position focuses on Sustainable Space, addressing critical challenges such as orbital debris management, space traffic optimisation, or deep-space exploration. Your research will
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. Elise Goossens and Prof. Dr. Britt Weyts. You combine a deep knowledge of the law with a research interest in the broader social context. For more information on the project, please contact elise.goossens
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of the electromechanical aspects of industrial machines, with an emphasis on Industry 4.0 technologies such as machine vision, AI or digital twins. A digital twin can be defined as a virtual replica of a physical system