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at the intersection of AI, RF, and wireless communication. Your main tasks include developing machine-learning methods for wireless interference detection, mitigation, edge intelligence, and applying AI to optimize RF
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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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materials, and will develop AI-driven Bayesian decision modelling for the optimization of experiments. Further, the candidate will support the development of safety formats and calibration of safety factors
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modeling, optimization techniques, hybrid testing and digital twins. Furthermore, the position aims at incorporating machine learning to drive innovation in the areas. Possible applications are within