Postdoc Scalable Graph Learning

Job description

Delft University of Technology (TU Delft) is seeking applications for a postdoctoral researcher in the area of Scalable Graph Learning, within the Department of Software Technology in the Faculty of Electrical Engineering, Mathematics, and Computer Science.

Graph machine learning (Graph ML) is a rapidly growing area of artificial intelligence (AI), driven by the widespread presence of graph-structured data across many real-world domains. Graph neural networks (GNNs) lie at the core of this field and have proven effective in a wide range of applications, including recommender systems, financial crime detection, cybersecurity, and network analysis. This project focuses on scalable, parallel, distributed, federated, and hardware-accelerated training and inference of GNNs and graph transformers, with applications in the financial domain. In particular, it targets the analysis of financial transaction networks for the detection and prevention of financial crime. Robustness and resilience under data heterogeneity and adversarial conditions will also be investigated.

About the Research Group

The Scalable Graph Learning Group (https://atasu-kubilay.github.io), led by Associate Professor Kubilay Atasu, is part of the Data-Intensive Systems Section (http://www.ds.ewi.tudelft.nl). The group focuses on both theoretical and practical aspects of Graph Machine Learning, including:

• Algorithmic efficiency
• Expressiveness
• Scalability
• eal-world applications

The Department Software Technology

The Department of Software Technology (ST) is one of the leading Dutch departments in research and academic education in computer science, employing over 150 people. The department ST is responsible for a large part of the curriculum of the bachelor’s and master’s programmes in Computer Science as well as the master’s programme in Embedded Systems. The inspiration for its research topics is largely derived from technical ICT problems in industry and society related to large-scale distributed processing, embedded systems, programming productivity, and web-based information analysis.

Job requirements

We are looking for a candidate who satisfies the following requirements:

• a PhD degree in Computer Science, Mathematics, Electrical Engineering or a related discipline with a PhD thesis conducted in the field of machine learning, deep learning, or parallel & distributed computing,
• experience in the field of graph machine learning, federated learning, differential privacy, or adversarial robustness,
• hands-on experience with Deep Neural Networks using PyTorch or TensorFlow, and preferably with GNNs using Pytorch Geometric or Deep Graph Library.
• first-author publications at leading conferences in artificial intelligence, machine learning, security and privacy, or data management.

Conditions of employment

• Duration of contract is 1 year Temporarily with a posibility for extension
• A job of 38 hours per week.
• Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
• An excellent pension scheme via the ABP.
• The possibility to compile an individual employment package every year.
• Discount with health insurers on supplemental packages.
• Flexible working week.
• Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
• Plenty of opportunities for education, training and courses.
• Partially paid parental leave
• Attention for working healthy and energetically with the vitality program.

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. .

Additional information
If you would like more information about this vacancy or the selection procedure, please contact Kubilay Atasu, via Kubilay.Atasu@tudelft.nl.

Application procedure
Are you interested in this vacancy? Please apply no later than 28 July 2026 upload the following documents:

• CV
• Motivational letter

You can address your application to Kubilay Atasu.

Please note:

• You can apply online. We will not process applications sent by email and/or post.
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