Postdoctoral position in computational modeling and simulation of spreading processes in population-scale multilayer social networks

Are you interested in working with computational methods for the study of social networks, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a postdoctoral position at Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

This Postdoc position is part of the eSSENCE graduate school in data-intensive science . The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven science applications. It is an arena where experts in computational science, data science and data engineering (systems and methodology) work closely together with researchers in (data-driven) sciences, industry and society to accelerate data-intensive scientific discovery.

eSSENCE is a strategic collaborative research programme in e-science between three Swedish universities with a strong tradition of excellent e-science research: Uppsala University, Lund University and Umeå University.

In this project we envision a novel collaboration between the Data Science and Intelligent Decision Making and the Applied scientific computing and High-performance computing eSSENCE research programmes. The postdoc will join the Information Laboratory (InfoLab) , an interdisciplinary research group where we do basic and applied research on the extraction of knowledge from social data.

Spreading on networks is a ubiquitous and fundamental process that underlies a wide range of phenomena in society, from the transmission of infectious diseases to the diffusion of information, opinions, and behaviors. These spreading processes are heavily constrained by social structure: different individuals and social groups have different probabilities of interacting, thereby creating (or limiting) pathways for spread. For example, segregation and stratification shape patterns of interaction in ways that can amplify or hinder the propagation of contagions or cultural influence, potentially leading to highly heterogeneous outcomes across populations. Understanding how spreading occurs at the scale of an entire population, and how it is shaped by network structure, is therefore essential for addressing major societal challenges, including epidemic control and the resilience of social and political systems. This project addresses two challenges in the study of population-scale spreading phenomena: computational scalability and the need to model spreading processes that traverse multiple network layers.

Duties

The aims of this project are

• To develop computational methods for simulating and inferring spreading processes in very large social networks with a multilayered structure.
• To apply selected models to a network encompassing the entire Swedish adult population, in order to study how different segments of may be affected differently by the spread of items such as diseases or political opinions.
• To derive and study effective equations models suitable for processes on multilayered networks. Such models allow the study of quantities that are difficult to evolve via direct simulations only.

Requirements

PhD degree in computer science, data science, computational social science, or related subjects or a foreign degree equivalent to a PhD degree in computer science, data science, computational social science, or related subjects. The degree needs to be obtained by the time of the decision of employment. Priority will be given to applicants who have completed their degree no more than three years before the deadline for applications.  Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

Other requirements are: experience working with social network data, excellent linguistic competences, excellent programming skills.

Additional qualifications

Experience in interdisciplinary research.

About the employment

The employment is a temporary position of 2 years according to central collective agreement. Full time position. Starting date January 2027 or as agreed. Placement: Uppsala.

For further information about the position, please contact:  Prof. Matteo Magnani (+46 18 471 40 21, matteo.magnani@it.uu.se) and Prof. Stefan Engblom (stefan.engblom@it.uu.se).

In this recruitment we have replaced the cover letter with questions that you are asked to answer when making your application. The answers will be used as a part of the selection process.

Please submit your application by 16 November 2026, UFV-PA 2026/3179.

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