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applications. These data consist of magnetic resonance sounding and gravimetric measurements, respectively detecting spatially varying water storage and temporal dynamics of groundwater masses. Ensemble Kalman
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comparison with the map and another between the vehicles. Regarding the multi-sensor data fusion method, preference will be given to Bayesian estimation methods such as the UKF (Unscented Kalman Filter) and
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should demonstrate research/educational background in preferably all of the following: Electrochemical physical modelling of LIB(P2D/SPM) Parameter identification State estimation(e.g. Kalman Filter
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offline according to historical data and current measures. Several techniques can be used: 1-step serial predictors (e.g. Kalman type), regressors (Gaussian Process regressor, ....) or extrapolators based
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Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ | Potsdam, Brandenburg | Germany | about 2 months ago
and by applying Kalman-Filter based data assimilation techniques to satellite data. Your responsibilities: Numerical and statistical modelling of large scale Earth systems, e.g., ocean, ionosphere
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at unraveling to what extent data assimilation methods such as ensemble Kalman filtering can be applied to improve our understanding of induced seismicity. As a case study, we will use available observations
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the underlying processes of these earthquakes. This PhD project aims at unraveling to what extent data assimilation methods such as ensemble Kalman filtering can be applied to improve our understanding of induced