Postdoc position in computational social science with a focus on Global North-South disparities in science

This postdoctoral position in computational social science requires expertise in analyzing complex social phenomena using computational methods. The focus on Global North-South disparities in science suggests a need for strong knowledge of international scientific collaboration patterns, research policy, and the socio-economic factors influencing scientific development across different regions. Candidates should be proficient in quantitative data analysis, including statistical modeling, network analysis, and possibly machine learning techniques applied to large-scale bibliometric or scientometric datasets. Experience with programming languages commonly used in computational social science, such as Python or R, is essential, along with familiarity with data visualization tools to effectively communicate research findings. A solid understanding of social science theories related to inequality, development, and science and technology studies will be important to contextualize computational results. The role likely involves interdisciplinary collaboration, requiring strong communication skills and the ability to work with diverse teams. While specific experience and education levels are not detailed, a PhD in computational social science, sociology, information science, or a related field is typically expected for postdoctoral roles. Candidates should demonstrate the ability to design and conduct independent research projects, publish in peer-reviewed journals, and contribute to advancing knowledge on global disparities in science through innovative computational approaches.