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assessment. Design and train reinforcement learning agents to optimize operational safety. Build and validate dynamic Bayesian network models integrating empirical and synthetic data. Conduct scenario-based
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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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facilities. You will be responsible for designing and conducting experiments to assess environmental (e.g. temperature and oxygen) tolerances and identify optimal conditions for the species, ideally working
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of the BUG-ID Marie Skłodowska-Curie Doctoral Training Network (DN), funded by the European Commission. BUG-ID consortium has a mission to improve infection diagnostics. All BUG-ID PhD students will be
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at DTU within the BUG-ID network (DCs 2, 3, 12, 13): DC2: Infection biomarker discovery in chronic wound models DC3: Infection biomarker monitoring in environmental samples DC12: Optimizing bioreceptor
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Optimization, within the context of sustainable water-based cooling in megacities. The stipend is open for appointment from February 1, 2026. or soon hereafter. The duration of the position is three years
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offered within the Marie Skłodowska-Curie QuNEST – Quantum Enhanced Optical Communication Network Security doctoral training program (https://qunest.eu ) and the Villum Investigator Program: Power-Efficient
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), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued. Mathematical skills: Competence in mathematical modeling of dynamic systems and
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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
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and global networking opportunities, unique research infrastructure including a fully controllable MW-scale hybrid power plant at our campus, and a collaborative research environment. Job profile We