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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory
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the shift from the robust architecture of radio-TV transmitters and receivers to the far more complex and fragile structure of ‘over-the-top’ (IP-based) media delivery. The project will model optimal
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framework offering optimal inspection and maintenance decision support. Explore additively manufactured structural concepts, including performance trade-offs for lightweight, fatigue- and corrosion-resistant
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). The project envisions the use of methods such as: Large Language Models (LLMs) to hold a knowledge base of regulatory documents Reinforcement Learning (RL) for adaptive decision-making under uncertainty Dynamic
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optimization and enable automated diagnosis of performance-limiting factors and suggest optimization strategies. The project will be part of the DFF Sapere Aude project, and research stays abroad are part of
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engineering, or similar. Solid mathematical and analytical skills, including signal processing and optimization. Knowledge about classical and/or quantum data communication, including for instance error
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=ab369eed-9005-4b04-9b12-66e400036e1c ). The project aims to build fundamental knowledge on the biology and environmental tolerance of the lumpsucker (Cyclopterus lumpus ) — a culturally and ecologically
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performance. This topic is based on a Sapere Aude Research Leader grant from the Independent Research Fund Denmark: Optimizing Human-AI Interaction: Integrating Domain Knowledge into Causal AI Systems: Causal
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sensor integration. Experience with SLAM algorithms (vision-, acoustic-, or inertial-based), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued