PhD Candidate in Population-Scale Network Science

Laplace Apply; Leiden; À distance; EUR 36 000 - 45 000; Plein temps

Résumé du poste

Leiden University invites applications for a PhD Candidate in Population-Scale Network Science in Leiden. The project focuses on large-scale social network analysis and algorithmic network science, with LIACS involvement and access to population-scale data resources.

The candidate will conduct independent doctoral research, publish results, and teach a small amount during a four-year, salaried contract. The role requires a master’s degree in a relevant field with strong programming and

Qualifications

• Master’s degree in computer science, mathematics, statistics, computational social science or a relevant discipline with substantial programming and analytical preparation is required.
• Experience with graph algorithms, network modelling and large-scale data analysis is valued.
• Proficiency in Python or R and comparable languages for population-scale network science.

Responsabilités

• Independent doctoral work.
• Publication of research findings.
• Small amount of teaching duties.

Connaissances

Graph algorithms

Python

R

Network analysis

Longitudinal data

Statistics

Formation

Master's degree in CS/Math/Stats/Computational social science

Substantial programming & analytical preparation

Outils

Python

R

Description du poste

PhD Candidate in Population-Scale Network Science

Netherlands

Apply by 12 Nov 2026

About This Opportunity

Leiden University offers PhD Candidate in Population-Scale Network Science in Leiden, focused on large-scale social network analysis and algorithmic network science.

Leiden University is recruiting a doctoral candidate in population-scale network science. The research aims to develop methods for understanding patterns of social or population connectivity using large, longitudinal data and scalable network analysis. The project is associated with LIACS and population-scale data resources. Research may involve structural-equivalence concepts, network modelling, computational algorithms and collaboration with holders of restricted administrative or statistical datasets. The role includes independent doctoral work, publication and a small amount of teaching. A master’s degree in computer science, mathematics, statistics, computational social science or a relevant discipline with substantial programming and analytical preparation is required. The appointment is advertised as 1.0 FTE for four years, with some teaching duties. Compensation or funding: EUR 3,204–4,051 gross per month as a salaried doctoral candidate. Stated appointment or programme length: Four years. Application timing: 12 November 2026 at 23:59 CET shown by AcademicPositions; official employer vacancy specifies date only.

Who This Opportunity Is Best Suited For: Applicants with graph algorithms, statistics, network modelling and strong programming experience in Python, R or comparable languages may be particularly well prepared.

Potential Challenges / Who May Find It Less Suitable: Population-level data can be sensitive and methodologically demanding. This is a four-year doctoral employment commitment and requires a sustained interest in network-theoretic research rather than social-media analytics alone.

Overall Opportunity Assessment: The distinctive research challenge is scaling theoretically meaningful network models to large longitudinal population structures.

Large-scale social network analysis and algorithmic network science

Level PhD

Time Commitment Full-time

Duration Four years

Field Network Science

Specialization Computer Science

Organization Type University

Funding & Compensation

Amount EUR 3,204-4,051 / Per Month

Funding Type Salary

Funding Status Paid

Period Per Month

Requirements

• Education: A master’s degree in computer science, mathematics, statistics, computational social science or a relevant discipline with substantial programming and analytical preparation is required.


• Experience: Technical Skills – Network analysis, Python, R, Graph algorithms, Longitudinal data.