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  3. PhD in AI-Networking: Optimal Decisions and Dynamic Clustering

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PhD in AI-Networking: Optimal Decisions and Dynamic Clustering

The sheer quantity and complexity of interactions in our society (human, governmental, political, financial) and critical infrastructures (power grids, …

3 maanden geleden


Mekelweg, Delft, Zuid-Holland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
38 - 40 uur
€ 2325 - € 2972 per maand


The sheer quantity and complexity of interactions in our society (human, governmental, political, financial) and critical infrastructures (power grids, transport, water & gas networks, telecom) is dramatically increasing. Algorithms and intelligent agents are therefore being delegated the management and control of these systems.

The ability to accurately discover hidden relations between items that share similarities in particular is of paramount importance to these agents. Clustering algorithms in particular have become prevalent: once clusters of similar items have been identified, subsequent analysis and optimization procedures benefit from a reduction in dimensionality.

In the past, clustering algorithms ignored time-dependent structures within data. Recently however, we made state-of-the-art advances into the detection of clusters in so-called Block Markov Chains (BMCs). One can now observe just one trajectory of a Markov chain, and provably recover hidden clusters, all using our algorithm.

In light of this exciting development, you will now:
(1) Develop time-dependent clustering methods suitable for application in real-world networks and datastreams in collaboration with KPN.
(2) Push the scope of application of time-dependent clustering procedures into new theoretical directions, leveraging inspiration gained from the collaboration.
(3) Merge reinforcement learning with clustering techniques on BMCs, and perform regret analyses to quantify the efficacy of your new learning algorithms.

To achieve (1)-(3) the Networks Architectures and Services (NAS) research group invites applicants for this challenging PhD position. Your position will be part of NExTWORKx, an exciting collaboration between academia and industry:



The ideal candidate is brilliant, has a strong interest in theoretically developing, understanding, and implementing learning / clustering algorithms on networks and datastreams, and is well-versed in probability theory, statistics, machine learning and network science.


The TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit graduateschool.tudelft.nl/ for more information.

Additional information

For more information on the position, contact dr.ir. Jaron Sanders (j.sanders@tudelft.nl).

To apply, e-mail a letter of application and resume to dr.ir. Jaron Sanders with in cc: prof.dr.ir. P. Van Mieghem (p.f.a.vanmieghem@tudelft.nl). Refer to vacancy number EWI2019-07. This vacancy closes on the 15th of April, 2019.

Being an EU citizen is an advantage.