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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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. It aims at elucidating fundamental physico-chemical parameters into play in these batteries to propose improvements of cell components, i.e., electrode materials and electrolyte. Possibility to extend
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of main control parameters as well as different patterns and amplitudes of the heat flux on the outer boundary. The candidate will analyze these simulations to establish the dependence of the dynamo regime
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and localization of a potential fault using the Matched Field Processing (MFP) method, based on the reconstruction of a response model of the inspected structure from the modal parameters predicted by
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on estimated movements using eDCCs. The research will focus on data simulated using the Monte Carlo method and real data from clinical SPECT scanners with a parallel collimator, such as those available at LUMEN
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will estimate the Seebeck coefficient, the electrical conductivity, and the thermal conductivity, and assess how changes in molecular structure, packing, and doping influence these properties. Key tasks
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Processing Skills required: - Medical computer programming: python, 3D slicer, LCmodel (optional), FSL, spm, ants) - Artificial Intelligence skills and deep learning experience - Proficiency in Tensorflow