In order to predict the functional properties of heterogeneous materials through numerical simulation, reliable data on the spatial arrangement and properties of the constitutive phases is needed. A variety of experimental tools is commonly used at the laboratory to characterize spatially the physical and chemical properties of materials, generating "hyperspectral" datasets. A path to progress towards an improved undestanding of phenomena is the combination of the various imaging techniques using the methods of data science. The objectives of this post-doc is to enrich material knowledge by developping tools to discover correlations in the datasets (for exemple between chemical composition and mechanical behavior), and to increase reliability and confidence in this data by combining techniques and physical constraints. These tools will be applied to datasets of interest regarding cementitious materials and corrosion product layers from archaeological artifacts.