Postdoc Inference of Shortest Paths in Complex Networks

Postdoc Inference of Shortest Paths in Complex Networks

Are you excited by the challenge of designing scalable inference methods in complex network spaces? Do you thrive at the intersection of machine learning and network science, and want your work to contribute to cutting-edge research in fields like telecommunications, bioinformatics, and AI?

Job description

Join our ambitious research team as a postdoctoral researcher in the prestigious NWO VICI project on geometric approaches to shortest paths in complex networks!

The Project: A New Perspective on Shortest Paths

Shortest paths are central to a wide range of disciplinary and interdisciplinary challenges — from transportation routing and cell signaling to social dynamics and the spread of epidemics. Classical algorithms solve shortest-path problems efficiently on complete, static networks. However, many real-world networks (e.g., the Internet, protein interaction networks, social networks) are incomplete, dynamic, or both.

This project investigates a machine learning–based alternative to shortest-path inference, focusing on non-Euclidean embeddings of networks. The overarching goal is to understand and predict shortest paths when the network is only partially known or constantly changing.

We are building a dynamic and collaborative team of two Ph.D. candidates and two postdoctoral researchers. This position is specifically for a postdoc working on inference aspects of shortest-path geometry.

Your Role

You will develop scalable methods to learn non-Euclidean representations (embeddings) of large, incomplete networks and to infer shortest paths within them. The project focuses on the interplay among:

• Scalability
• Inference accuracy
• Inhomogeneous character of missing network data

You will work in close collaboration with other team members, including another postdoctoral scholar focusing on the theoretical aspects of shortest paths in geometric network models.

You will also have the opportunity to explore applications of shortest-path inference in fields such as Telecommunications and Bioinformatics, and to co-supervise Ph.D. students working on related topics.

Active participation in international conferences, workshops, and collaboration visits is an integral part of this role.

What We Offer

• A full-time postdoctoral position (1 year, with potential extension for 1–2 years based on performance)
• A stimulating and inclusive research environment at TU Delft
• A prestigious, well-funded project with international visibility
• Opportunities for collaboration, conference travel, and interdisciplinary work
• Support for personal and professional development, including mentoring and supervision experience

We strongly believe that diversity enriches science. We therefore warmly welcome applicants from all backgrounds. We are committed to creating an inclusive and supportive research culture where everyone can thrive and feel heard.

Who Are We Looking For?

We’re looking for a curious and motivated researcher with the following profile:

• A Ph.D. (or near completion) in network science, statistical physics, mathematics, computer science, or a related field
• A strong background in graph theory and probabilistic network modelling
• Familiarity with non-Euclidean geometry and network embeddings
• Extensive experience with network modeling and programming skills in Python, C, or C++
• A track record of peer-reviewed publications, ideally in network science or graph theory
• Enthusiasm for presenting at conferences and workshops
• A collaborative mindset and interest in co-supervising Ph.D. and M.Sc. students
• Excitement for working on high-risk, high-reward research with real-world impact

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Additional information

If you would like more information about this vacancy or the selection procedure, please contact Dr. Maksim Kitsak, via m.a.kitsak@tudelft.nl.