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adaptation across operating regimes, and physics-guided ML. State estimation and soft sensing: real-time estimation using Kalman-filter variants (EKF/UKF/augmented KF), probabilistic inference, and multi
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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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