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off-the-shelf sensors and the development of resilient algorithms that combine first-principles modeling with modern machine learning techniques. The goal is to push the boundaries of robust perception
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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Identities, Machine Learning/AI, and IoT/5G on organisations from both the private and public sectors. The group consists of doctoral and post-doctoral researchers from diverse backgrounds united in pursuit
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information about the role, please contact Prof. Radu State Your profile Strong background in AI, machine learning, or multi-agent systems, ideally with interest in financial systems, decentralized ledgers
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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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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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offer A stimulating learning environment. Here post-docs and professors outnumber PhD students. That translates into access and close collaborations with some of the brightest ICT researchers, giving you
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onboarding period that includes specialized courses and hands-on training in AI and machine learning. You'll also have the chance to explore different labs and core facilities, meet fellow researchers, and
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the controlled flow at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning