Monthly Archives: March 2023

Two new PhD calls (Application deadline: April 1st)

We have two PhD positions open! Both salary and working conditions are excellent. Our group is a down-to-earth and fun place to be. Copenhagen is often named the best city in the world to live in, and for good reasons. It’s world-renowned for food, beer, art, music, architecture, the Scandinavian “hygge”, and much more. In Denmark, parental leave is generous, and child-care is excellent and cheap. See the two positions below, and feel free to apply if you fit (you can apply to both).

Position number 1

This PhD will work under the supervision of Roberta Sinatra, will be employed at SODAS (University of Copenhagen), and will have affiliations with the NEtwoRks, Data, and Society (NERDS) group at IT University of Copenhagen and with the pioneer center for AI. The topic is on Science of Science and Algorithmic fairness. The PhD position is part of a large project, funded by the Villum Foundation, aimed to uncover the bias mechanisms that drive scientific impact, and to use them to create fair algorithms. The project will involve the analysis of large-scale datasets, running controlled experiments, and modelling social dynamics in science. Our priority is to attract technically strong researchers who are interested in asking bold, new questions with data. The team executing the project is composed of the PI, two postdocs, and one PhD student. 

Apply here by April 1st 2023: https://jobportal.ku.dk/videnskabelige-stillinger/?show=158564
Contact Roberta Sinatra (robertasinatra@sodas.ku.dk) if you have any questions

Position number 2

This PhD will work under the supervision of Vedran Sekara, with co-supervisor Roberta Sinatra, will be employed in the NEtwoRks, Data, and Society (NERDS) group at IT University of Copenhagen, and will have an affiliation with the pioneer center for AI. The PhD position is funded by the pioneer center for AI and the topic is predictability of social systems. Indeed, with the rise of algorithmic decision-making and with automated systems mediating an increasingly larger part of our social, cultural, economic, and political interactions, it is vital to understand the limits of prediction and when predictive accuracies fall short of expectations. The overreaching goal of this proposal is to develop an empirical and theoretical understanding of predictability in social networks and human mobility. Are prediction limits determined by the size and bias present in datasets, the scale of computational power, or are there fundamental limits to prediction?

Apply here by April 1st 2023, make sure to specify the project (last one listed): https://candidate.hr-manager.net/ApplicationInit.aspx?cid=119&ProjectId=181550&DepartmentId=3439&MediaId=5
Contact Vedran Sekara (vsek@itu.dk) if you have any questions.

 

Welcome Iraklis to NERDS!

Iraklis Moutidis joins NERDS today as a postdoctoral researcher. He will work with Luca Aiello on the COCOONS project. Iraklis got his PhD in Computer Science from the University of Exeter (UK) and he works at the intersection of Machine Learning and Social Network Analysis. Welcome, Iraklis!

New NERDS paper: Quantifying Ideological Polarization on a Network

Today we published a paper on ideological polarization. Special congrats to Marilena for this being her first paper!

Quantifying Ideological Polarization on a Network Using Generalized Euclidean Distance, by M. Hohmann, K. Devriendt, M. Coscia, published in Science Advances


An intensely debated topic is whether political polarization on social media is on the rise. We can investigate this question only if we can quantify polarization, by taking into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. Current polarization estimates are insensitive to at least one of these factors: they cannot conclusively clarify the opening question. Here, we propose a measure of ideological polarization which can capture the factors we listed. The measure is based on the Generalized Euclidean (GE) distance, which estimates the distance between two vectors on a network, e.g., representing people’s opinion. This measure can fill the methodological gap left by the state of the art, and leads to useful insights when applied to real-world debates happening on social media and to data from the US Congress.