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Postdoc in Population-Scale Network Science Leiden University in Netherlands

Degree Level

Postdoc

Field of study

Computer Science

Funding

Funded via the Macroscope national research infrastructure funded by the Dutch Science Foundation (NWO). Salary is €3,706–€5,760 gross per month based on a full-time 38-hour appointment, with holiday allowance, end-of-year bonus, pension scheme, commuting reimbursement, and other employment benefits.

Deadline

Nov 12, 2026

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Country

Netherlands

University

Leiden University

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Keywords

Computer Science
Data Science
Sociology
Statistical Analysis
Network Analysis
Computational Social Science
Statistics

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About this position

This postdoc at Leiden University focuses on developing new network science methods for analyzing longitudinal, population-scale social network data on the Dutch population. The project addresses the methodological challenges of very large, time-sensitive datasets by creating new metrics, fast algorithms, and careful adaptations of established network measures.

The research aim is to better understand how social network structures change over time through social and demographic dynamics, and how those changes connect to wider societal phenomena. The position sits at the intersection of methodological network analysis and substantive social science, with room for the postdoc to shape a direction that fits both their interests and the broader goals of the group.

The work is embedded in the Leiden Institute of Advanced Computer Science (LIACS), the computer science and AI institute of Leiden University’s Faculty of Science. The postdoc will join the interdisciplinary PLANET-NL initiative and the Leiden Network Science group, working in a highly international environment with opportunities for collaboration through the Leiden Complex Networks Network (LCN2) and the Dutch Network Science Society.

The project is linked to Macroscope, a national research infrastructure for studying complex societal dynamics at unprecedented scale using population-scale datasets, advanced computational tools, data, models, and computing resources. The position is specifically about developing graph analytical methods for population-scale social networks.

The postdoc will also contribute to limited co-supervision of BSc, MSc, and PhD students in data science and network science, and take part in minor scientific organizational activities within the group and wider research environment.

Funding details

Funded via the Macroscope national research infrastructure funded by the Dutch Science Foundation (NWO). Salary is €3,706–€5,760 gross per month based on a full-time 38-hour appointment, with holiday allowance, end-of-year bonus, pension scheme, commuting reimbursement, and other employment benefits.

What's required

Applicants must have a PhD in computer science or a related field such as mathematics, physics, or computational social science, with a track record in network science, complex networks, or social network analysis. Excellent English writing skills are required, evidenced by published first-author papers in respected conference proceedings or journals. Candidates should have affinity for bridging methodological advances in network science with substantive social science questions using population-scale data, an interdisciplinary mindset, experience programming in Python or alternatively R and/or C++, and the ability to work independently, collaboratively, and in a student co-supervisory role. Excellent candidates who are not yet past the PhD phase but expect to graduate in early spring 2027 may also apply.

How to apply

Apply online via the vacancy's blue button on AcademicTransfer. Attach a cover letter, an academic CV with a working Google Scholar profile URL, a recent writing sample, and contact details for at least two referees. If applicable, be prepared for exploratory conversations before the closing date and interviews on the listed dates.

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Professors

Prof.dr. at Leiden University

University Name
Country flag

Netherlands

Research Interests

Organizational Behavior

Social Media Marketing

Network Analysis

Computer Science

Network Dynamics