Postdoctoral position in Network Science - Machine Learning
The Mapping Complexity Lab of Prof. M. Ángeles Serrano and Prof. M. Boguñá is opening a call to hire a postdoctoral researcher in the Department of Condensed Matter Physics at the University of Barcelona.

Requirements:
A PhD in physics, computer science, computer/electronic engineering, mathematics, or other related disciplines.
Interest in interdisciplinary research, curiosity about AI and networks, high motivation to learn, an open-minded and collaborative spirit.
Excellent software development skills.
Excellent communication skills, and proficient in the English language, both written and spoken.

Offer:
The successful applicant will work with Prof. M. Ángeles Serrano and Prof. Marián Boguñá at the interface between Network Science and Machine Learning. The goal is to merge the best of the two worlds to produce a new generation of models and methods for the classification and prediction of complex networks. We offer a 2-year position (1+1) with a competitive salary.

Application process:
Interested applicants are requested to submit a Curriculum Vitae including relevant publications and the name and contact details of 2 referees.
We promote diversity and equal opportunities, minorities in science are encouraged to apply.
Queries about this position should be sent to marian.serrano@ub.edu or marian.boguna@ub.edu

Almagro, P.; Boguñá, M.; Serrano, M. A. «Detecting the ultra low dimensionality of real networks». Nature Communications 13, 6096 (2022)

https://www.nature.com/articles/s43588-022-00362-6

https://www.ub.edu/web/ub/en/menu_eines/noticies/2022/10/046.html

Recent advances in network science include the discovery that complex networks have a hidden geometry and that this geometry is hyperbolic. Studying complex networks through the lens of their effective hyperbolic geometry has led to valuable insights on the organization of a variety of complex systems ranging from the Internet to the metabolism of E. coli and humans. This methodology can also be used to infer high-quality maps of connectomes, where brain regions are given coordinates in hyperbolic space such that the closer they are the more likely that they are connected. Even if Euclidean space is typically assumed as the natural geometry of the brain, distances in hyperbolic space offer a more accurate interpretation of the structure of connectomes, which suggests a new perspective for the mapping of the organization of the brain’s neuroanatomical regions.

In humans, this approach has been able to explain the multiscale organization of connectomes in healthy subjects at five anatomical resolutions. Zoomed-out connectivity maps remain self-similar and our geometric network model, where distances are not Euclidean but hyperbolic, predicts the observations by application of a renormalization protocol. Our results prove that the same principles organize brain con-nectivity at different scales and lead to efficient decentralized communication.

If you want to see more:

Geometric renormalization unravels self-similarity of the multiscale human connectome

PNAS 117 20244-20253 (2020)    

Navigable maps of structural brain networks across species

PLoS Computational Biology 16 e1007584 (2020)