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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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Hamburg, ESRF in Grenoble and others) analysis of the experimental data, ideally connecting to our machine learning tools presentation of scientific results on conferences and in publications supervision
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19.07.2022, Academic staff The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current focus on deep
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