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that reduce raw data at the sensor level. You will develop AI and machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability
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, boosted by AI-data augmentation for extrapolating spectrum patterns from multiple sources. To design a scalable computing framework using a physics-informed neural network for distributed spectrum analysis
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versatility of deployed wireless systems. You study how to implement both localization and radar sensing capabilities using a single radio platform. Traditional localization systems typically rely on multiple
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machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability, these models will also be optimized to run efficiently on edge hardware
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monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
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multiple-antenna communication schemes and related architectures. Analog components also differ in performance over the FR3 band. For instance, different types of power amplifiers and related semiconductor
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efficient decoding algorithms" supported by the Luxembourg National Research Fund (FNR). The APSIA Group is seeking a highly qualified post-doctoral researcher for this project. For further information, you
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. Traditional localization systems typically rely on multiple anchor nodes—often three to four—strategically placed around the perimeter of the coverage area. In contrast, your approach leverages a single