This project aims to uncover the biophysical principles that enable mammalian embryonic stem cells to self-organize into three-dimensional structures resembling developing embryos. How do cells acquire their identities and positional information, and how does precise and reproducible spatial organization emerge from their collective behavior? Combining quantitative microscopy, image analysis, mathematical modeling, and machine learning, the project will investigate the emergence of positional and correlative information during stem-cell differentiation and the flow of information through genetic networks. By linking theory and experiments, we aim to identify general principles of developmental self-organization and compare information processing across different biological systems, including mammalian stem-cell aggregates and Drosophila embryos.