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to develop and implement state estimation algorithms, such as Kalman filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using
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on the development and implementation of Kalman Filtering workflows to be applied to satellite altimetry data acquired over the polar ice sheets. Joining the UK’s Centre for Polar Observation and Modelling
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Estimation & Observers: Design and implement state estimators, Extended Kalman Filters (EKF), neural observers, and physics-informed "virtual sensors" for real-time plasma state estimation and boundary
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and ambiguity function processing Familiarity with time-, frequency-, and amplitude/phase-difference of arrival for geolocation Understanding of adaptive signal processing methods and techniques (Kalman
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and ambiguity function processing Familiarity with time-, frequency-, and amplitude/phase-difference of arrival for geolocation Understanding of adaptive signal processing methods and techniques (Kalman
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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knowledge in data assimilation and Kalman filters Working experience with atmospheric or climate data Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written
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; Knowledge of TDOA-based localization, multilateration, or positioning systems; Experience with Kalman filters, nonlinear optimization methods, or target tracking algorithms; Knowledge of distributed
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration