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The hired postdoctoral researcher will mainly work on WP2, i.e., on the development of new formalisms and methods to apply to higher order interaction patterns identified in the data analyzed in WP1. The project aims to build a theoretical and data analysis framework to demonstrate the role of higher-order interactions (HOIs) in human brain networks supporting causal learning. The Hinteract project includes three scientific work packages (WPs): WP1 focuses on developing an informational theoretical approach to infer task-related HOIs from neural time series and characterizing HOIs supporting causal learning using MEG and SEEG data. WP2 involves developing a network science formalism to analyze the structure and dynamics of functional HOIs patterns and characterizing the hierarchical organization of learning-related HOIs. WP3 is about compiling and sharing neuroinformatics tools developed in the project and making them interoperable with the EBRAINS infrastructure.