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organization. About the position The Nansen Centre has long expertise in data assimilation (DA) and climate predictions, initially introducing the Ensemble Kalman Filter (EnKF) data assimilation method in
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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
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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enabling robust real time field reconstruction. Within this objective, techniques such as dynamic reduced order models, Kalman filtering and Physics Informed neural operators/networks will/can be employed
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, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence