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improve the efficiency, maneuverability, and noise performance of drones and other multirotor aircraft, but their deployment requires more advanced modeling and control methods. The PhD will focus
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partially filled containers – is a major challenge in aerospace, transport, and energy systems, where it can compromise stability and safety. The PhD will focus on developing low-order models of sloshing
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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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laude level. You are interested in both Machine Learning and Symbolic/Logic-based AI methods. You strive for excellence and have a scientific mindset. You are a loyal team player, who can work
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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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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neuromorphic ultra-low-power active sensor readout and processing at the edge. The chip design will enable online learning capabilities, aiming at modulating the spatio-temporal filtering properties with
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, GenAI, and IoT/5G
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military performance, presenteeism, and may even lead to costly evacuations during deployments. This PhD project builds upon pioneering research at MHQA and aims to implement and evaluate allergy screening
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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