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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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This multidisciplinary thesis requires strong expertise in several of the following areas: Robotics, computer vision, control systems, dynamic modeling, signal processing, or machine learning
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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and signal processing, machine learning is now a global field supported by major international conferences such as AISTATS, NeurIPS, ICML, and MVML. Applying these methods to the study of Paleolithic