Master Thesis

vor 2 Wochen


Munich, Deutschland Technische Universität München Vollzeit

Geometrical and topological machine learning techniques have established themselves as expressive techniques for extracting meaning from complicated data sets. Recently, they have seen increasing use to characterize data sets in neuroscience, leading to stable representations. In this project, we are interested in tackling the analysis of large-scale EEG and optical imaging data. Finding common representations for these two data acquisition modalities will contribute new insights into the mind.

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