Young Talent Prize 2020

We have received only excellent applications for our first ever Young Talent Prize: the candidates were without exception of high quality and are each unique in their contributions to network science. The winner of the 2020 Young Talent Prize, Clara Stegehuis, not only exemplifies the excellent interdisciplinary representation of mathematics within Dutch Network Science, but is also a clear role model for the field through her outreach to the general public in general, and to young generations in particular. We congratulate Clara with this award and look forward to her presentation during our Young Talent Symposium on November 2nd! Click here for more information about the Young Talent Symposium.

Dutch Network Science Society Young Talent Prize 2021

Nominations for Young Talent Prize 2021 are open now till 15th September 2021. More information can be found here.

Two PhD positions in Global Environmental Governance at Utrecht University

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3-year postdoctoral position in in integrative decision-support methods

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Postdoctoral position in developing synergistic, multiplex disease networks

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PhD/Postdoc position: Networks and value chains in organized crime

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Postdoc position at the Computational Urban Science & Policy lab

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PhD student or Postdoc position at University of Amsterdam: Networks and value chains in organized crime

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PhD student position at Eindhoven University of Technology

At Eindhoven University of Technology, shared between the Electrical Engineering and Mathematics and Computer Science Departments, there is a vacancy for a PhD student. The intended supervisors are George Exarchakos (EE) and Remco van der Hofstad (MCS). The project will focus on distributed ways to measure centrality in networks, for example using the Game of Thieves protocol. In this protocol, several walkers run through the network looking for commodities, and, upon finding them, the commodities are moved to the starting point of the walks. The most central nodes are the ones that receive the least commodities. The aim of the project is to describe this centrality measure, possibly using local weak convergence techniques, as well as to study what happens when central nodes are being removed. We are also interested in the dynamical properties of this centrality measure, in particular what happens when the graph changes over time while the walkers run around on the network. 

We aim to approach these problems in a mathematically rigorous way, and we expect the candidate to have some experience in formally proving results. Some experience with simulations is welcome, though not required.

These questions are spurred by modern communication. Future communication networks are expected to be ultra dense in space and pervasive to our living spaces enabling far better coverage and higher bandwidth anywhere. Smart radio environments will play an important role to achieve this goal. Yet, their control has to become inherently distributed, autonomous and intelligent. The controllability of wireless mesh networks heavily depends on the properties of the graphs they form and their ability to coordinate in a local way. New intelligent methods are needed to achieve self-awareness of large-scale random wireless networks and to estimate the impact of a topology change. This project will study the fundamental models needed to achieve intelligence embedded at the nodes of the network. 

This PhD project is within the NETWORKS Gravitation program NETWORKS, and the student will be expected to actively participate in this program.