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Leiden Computational Network Science

Network science research group at Leiden University

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Job opening: PhD Candidate in AI for Network Analysis

May 27, 2025 network science

Are you eager to contribute to cutting-edge research at the intersection of AI and network science/social network analysis in a vibrant academic environment? Leiden University invites applications for a PhD position in AI for Network Analysis, starting as early as September 1, 2025.

We are looking for a motivated and talented early-career researcher with a strong interest in social network analysis, complex networks, or network science, and relevant background knowledge on methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods. Examples of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data. The candidate is furthermore encouraged to propose their own research direction related to AI methods for advancing network analysis.

We are looking for
An excellent young scientist with a master’s degree or equivalent in computer science, data science, AI, computational social science, or a related field. You have a keen interest in algorithms, ML, and AI, well-developed skill set in (scientific) programming, and an affinity with network analysis in particular. You are able to work with very large data and have the creativity and curiosity to develop AI models for network analysis. The ideal candidate has a critical, open-minded, and collaborative spirit; is creative, precise, and does not shy back from entering new scholarly terrain at the cutting edge of network science and AI.

For more information, see the university webpage: https://www.universiteitleiden.nl/vacatures/2025-nl/q2/15718phd-candidate-in-ai-for-network-analysis

All applications run through the university job application portal.

Leiden Network Science group at Netsci 2025 in Maastricht

We will be present at Sunbelt 2025 in Paris

About the CNS Group

The Leiden Computational Network Science (CNS) group is a research group working on methods and algorithms for analyzing  real-world (social) network data. We develop techniques to unveil patterns in dynamic complex networks from a range of application domains, including online soclal networks, population-scale networks, scientific collaboration networks and economic networks. The CNS group is lead by Frank Takes and hosted at LIACS, the Department of Computer Science and AI of Leiden University.

 

 

Leiden Network Science Follow

News & updates about (computational) network science, complex networks and social network analysis; curated by @franktakes & Leiden group members

funwithnetworks
Retweet on Twitter Leiden Network Science Retweeted
planet_nl Platform for Large-scale Analysis of Networks @planet_nl ·
4 Nov

We’re pleased to (re)introduce the PLANET-NL group, formerly POPNET.
Our researchers are continuing to collaborate to provide expertise, tools, and opportunities for large-scale social network analysis.
Be ready to get to know more about us in the coming days!

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funwithnetworks Leiden Network Science @funwithnetworks ·
26 May

📢📢📢Join us for the Algorithmic Fairness in Network Science Satellite at @NetSci2025 on Tuesday 3 June. Details and schedule available at https://fairnetsci.github.io
#netsci #fairness

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funwithnetworks Leiden Network Science @funwithnetworks ·
26 May

Exciting times! Many members of CNS are going to present their research at @NetSci2025! Here you can find the complete schedule.
See you there in a week! 🔥🔥🔥

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Retweet on Twitter Leiden Network Science Retweeted
saxena_akrati Akrati Saxena @saxena_akrati ·
24 Jan

🚨📢 Call for Abstracts! 📢🚨

Join us for the Algorithmic Fairness in Network Science Satellite at NetSci25

💡Submit your abstract and be part of this exciting conversation!

👉Details: https://fairnetsci.github.io/

📅 Deadline: February 7, 2025

#Callforabstract #netsci #fairness

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