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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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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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background in machine learning, including Natural Language Processing. You have excellent coding skills in Python; hands-on experience in deep learning frameworks such as PyTorch or Tensorflow is a plus You
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Job description PhD position in computational neuroscience - Deep reinforcement learning closed-loop control for the treatment of epilepsy As part of the highly prestigious ERC Starting Grant
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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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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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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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. Basic knowledge in sensing technologies and measurement systems. Basic knowledge in machine learning, deep learning and/or data fusion and modelling tools, and eager to learn about more advanced modelling