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. Desirable Criteria Experience implementing machine learning or deep learning models (e.g., neural networks, probabilistic learning methods). Knowledge of state estimation techniques, such as Kalman filters
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state estimation and multi-target tracking algorithms (e.g., Kalman/particle filters, Gaussian mixture filters, random finite set methods, MCMC-based approaches) for SSA/SDA and aerospace applications
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of large data sets. Determining fundamental and technical limits of a measurement, using principles such as the Cramer Rao bound and Fisher information, Gaussian process, Kalman filter, and state estimation
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