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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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tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement learning strategies for comb generation
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focus on the following areas: Subspace tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement