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battery systems, BMS, and battery models, including equivalent circuit and electrochemical models. Proven ability to develop and implement state estimation algorithms, such as Kalman filters and observers
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optimisation, 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
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sensing techniques (e.g., Kalman Filters) combined with multi-nature sensing data from optimally chosen locations Development and application of advanced fracture mechanics models for crack
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, air quality, or greenhouse gases. Data assimilation methods, including variational, ensemble, Kalman filter, or hybrid approaches. Atmospheric or chemical transport modelling and inverse modelling
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virtual sensing techniques (e.g., Kalman Filters) combined with multi-nature sensing data from optimally chosen locations Development and application of advanced fracture mechanics models for crack
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Requirements Required experience includes: Significant experience implementing high performance sensor fusion and state estimation algorithms in real systems using, e.g., Kalman filters, particle filters, etc
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, Extended Kalman Filters (EKF) and Factor Graph Optimization (FGO); Testing and comparing different Simultaneous Localization and Mapping (SLAM) methods suitable for forest environments and smartphone
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(e.g. Kalman filtering), data driven methods (e.g. machine learning or neural networks), etc. The experience could come from lithium-ion, lithium-ion capacitor, sodium-ion, or other battery
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prediction 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