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, 3, 12, 13): DC2: Infection biomarker discovery in chronic wound models DC3: Infection biomarker monitoring in environmental samples DC12: Optimizing bioreceptor function in interaction with graphene
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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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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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developed to allow operators to visualize real-time risk indicators, maintenance sched-ules, and preventive repair alerts, facilitating optimized maintenance decisions and reducing unplanned downtimes
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the identification and development of data-driven models that can subsequently be used for system optimization. The project encompasses both algorithm design and experimental implementation. Close collaboration and
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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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optimal control, robust control, distributed control, and model predictive control. A background in power system analysis, dynamics, control and/or converter control is considered a strong advantage
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of millions of combinations of control parameters, determining the optimal values to maximize stability, efficiency, and dynamic performance. The ideal candidate will have experience or an interest in
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, current methods are either static, rely on heavy offline training, or fail to adapt to changing environments. This PhD project will develop intelligent software agents capable of autonomously optimizing