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parameters. The model is used for condition monitoring and fault detection using methods focusing on statistical methods using residual generation and Kalman filtering. Qualifications: Phd and master's degree
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Julia and/or Matlab and/or C++. In addition to the above, to be successful for Level B you will also need: Experience in state estimation, target tracking, and data fusion algorithms such as Kalman
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estimation, target tracking, and data fusion algorithms such as Kalman filtering, Particle filtering, and Multiple-hypothesis tracking. A strong track record of high quality research as evidenced by
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modeling. State estimation will employ control engineering methods, such as Kalman filters, and may alternatively explore cutting-edge machine learning techniques. The development of models and estimation
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simulators (VISSIM, AIMSUM, SUMO). Experience with optimal control, model-predictive control, optimization, Kalman filter. Experience with AI techniques such as reinforcement learning. Programming in C/C
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will join our interdisciplinary research team to develop an Ensemble Kalman Filter (EnKF)-based coupled data assimilation capacity for the DOE’s Energy Exascale Earth System Model (E3SM) and the regional
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observations using a Local Ensemble Transform Kalman Filter (LETKF) approach. Data assimilation is also used by the AC team to compute retrospectives analyses of the atmospheric composition or to estimate dust