Phd Student in Network Science

Institute of Mathematics_
**PhD Student in Network Science**
**100 % **:Two fully funded PhD positions are opening in the new Quantitative Network Science group of Prof. Alexandre Bovet at the Department of Mathematics in the Faculty of Science of the University of Zurich (UZH).
Network science has emerged as a powerful framework to study complex systems by representing their components and the interactions between them as mathematical graphs, and thus emphasizing the key role of connectivity on their behaviour. Nodes of a network may represent individuals, companies, neurons, genes or words while edges represent their relations which may refer to social influence, economic transactions, activity correlation, regulation or co-occurrence, depending on the context.
Our research group aims at closely combining novel theoretical and methodological developments in network science with data science approaches to gain unique insights into the functioning of complex systems.
Research directions include the mathematical modeling of dynamical processes occurring in networks and the development of novel methods to investigate temporal networks. In particular, we develop methods to extract a simplified view of the structure and dynamics of complex systems.
A focus is also put on investigating the impact of digitalisation on society through the Digital Society Initiative. Potential research directions include issues related to mobility, health, work, democracy and communication. In particular, we are interested in computational social science approaches to understand how information and misinformation spread in social networks and to measure its impact. We are also interested in developing network science methods to analyse the polarisation and the opinion of users on online social networks.
To get an idea of the research questions you would be involved in, please have a look at the following publications:

- Gutiérrez-Gómez, L., Bovet, A. and Delvenne, J.C. (2020) 'Multi-Scale Anomaly Detection on Attributed Networks', Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), pp. 678-685. doi:10.1609/aaai.v34i01.5409.
- Bovet, A. and Makse, H.A. (2019) 'Influence of fake news in Twitter during the 2016 US presidential election', Nature Communications, 10(1), p. 7. doi:10.1038/s41467-018-07761-2

**Your profile**:

- Master degree in Mathematics, Physics, Data Science, Computer Science or related fields.
- Strong interest in developing research in an interdisciplinary context.
- Passion for sharing knowledge and teaching.
- Strong programming skills.
- Strong scientific writing skills or willingness to develop them.
- Very good expressiveness in English

**What we offer**: We offer a collaborative, inclusive and interdisciplinary research environment with plenty of opportunities to develop your academic career. In addition, you will have diverse opportunities for networking with international and interdisciplinary researchers, as well as support for professional travel and research needs. Furthermore, the doctoral program will allow you to develop and strengthen your research skills.
The University of Zurich is one of Switzerland's leading research universities with excellent working conditions, competitive salary, and generous social benefits. Conditional on approval of the PhD proposal by the end of the first year, funding is guaranteed for four years total.

**Place of work**: Irchel campus, UZH, Zurich, Switzerland

**Start of employment**: 1st of July or by agreement. Evaluations will continue until the position is filled.