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into **influence functions**, theoretical tools designed to quantify the impact of a sample on a machine learning model. These functions, defined through the derivative of model parameters or the loss function with
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an internationally recognized research team at LAAS-CNRS in Toulouse, focused on developing autonomous mobile machines that integrate perception, reasoning, learning, action, and reaction capabilities
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published openly in the form of process flowsheet databases. Skills: Machine Learning/Deep Learning skills are essential, as well as programming proficiency, as well as some knowledge of energy or process
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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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correlations or more innovative methods of multivariate analysis and we anticipate here an opportunity of using machine learning that could help in predicting properties or classifying sources. A last step will