The lived experience of urban life is deeply shaped by how individuals move. In recent years, computational social scientists have increasingly leveraged mobile phone mobility data to study social and urban phenomena in a more dynamic and granular way. This data-driven approach has provided insights into accessibility to opportunities, urban resilience and evacuation patterns, socioeconomic inequality and segregation, crime, and public health dynamics. The goal of this Ph.D. thesis is to investigate the extent to which the study of human mobility can enhance our understanding of human behavior. Specifically, this research will aim to develop computational methods that capture the complexity of urban movement and assess potential biases in the use of mobile phone data for studying mobility patterns. The ideal candidate should have a strong background in computational disciplines such as computer science, physics, data science, applied mathematics, or related areas. Additionally, an interest in interdisciplinary research at the intersection of computational social science and behavioral sciences is highly desirable. Contact person: Bruno Lepri ([email protected])