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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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the 01.02.2026 at the following conditions (PhD position): 50% = 19,92 hours Pay grade 13 TV-L limited 30.11.2028 Your tasks: Develop AI algorithms for real-time fault detection, fault classification
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the device and for algorithm efficiency as compared to qubits. We will explore the use of tightly focused laser beams and their interaction with crystals of trapped ions to realize new ways to prepare and
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(CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising data
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. Your duties and responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with
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developing technologies for 6G? To meet the requirements of future mobile networks, network architectures need to become more agile and will include terrestrial and airborne network components linked by radio
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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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electrochemical phenomena. · Propose non-invasive diagnostic methods based on this digital twin. · Develop algorithms to speed up or standardize the determination of the parameters of this digital
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). Information Key Responsibilities: Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring. Utilize a mixed approach combining real and synthetic data for algorithm development
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building