PhD student in Fair Social Network Analysis - Hiring in process/Finished, not possible to apply

Are you interested in developing computational Social Network Analysis (SNA) methods that aim to minimise bias against individuals or groups, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in a sustainable workforce and offers safe, favourable working conditions? We welcome you to apply for a PhD position at the Department of Information Technology, Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, with 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

The candidate will join the Uppsala University Information Laboratory (InfoLab), one of the research labs at the Computing Science Division, Department of Information Technology. At InfoLab we do basic and applied interdisciplinary research on knowledge extraction from social data.

Project description
The project is in the emerging area of fair social network analysis. In today’s algorithmically-infused society, data about our social relations is continuously collected, stored, and used to make decisions. For example, our online interactions (comments, likes, shares) are stored by social media platforms and used to recommend whom to follow and which content is shown to us. These recommendations are often based on social network analysis algorithms, which are used to compute features for all nodes in a social network based on their position.

Unfortunately, the broader societal perspective surrounding the algorithmic aspects of social network analysis is problematic and underdeveloped. Our position in our online and offline social networks is influenced not only by our choices as individuals, but (mostly) by our social identity, as defined by personal traits such as ethnicity, gender, age, and political views. This favours the emergence of unfair loops: our social identity influences our position in the network, determining the results of the analysis algorithms, which are in turn used to make decisions that reinforce our position in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop social network analysis methods that incorporate both network structure and the personal attributes of nodes, while minimising bias against individuals or groups.

The project will be developed with the following requirements:

• The definition of fairness implemented in the designed SNA methods must refer to the related sociological literature.
• Method development must be based on advanced combinatorial optimisation technologies.
• The methods must be tested on real data.

Duties 
The doctoral student will primarily devote their time to graduate education. Other departmental duties of at most 20%, including teaching and administration, may also be included in the employment.
 
Requirements 
Entry requirements for doctoral education are regulated in the Higher Education Ordinance. To meet the general entry requirements for doctoral studies, you must:

• hold a Master’s (second-cycle) degree in data science or related areas relevant for the PhD topic, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

The University may permit an exemption from the general entry requirements for an individual applicant, if there are special grounds (Chapter 7, § 39 of the Higher Education Ordinance). For special entry requirements, please see the subject’s general study plan. 

We are looking for candidates with:

• a strong interest in interdisciplinary research,
• excellent coding and modelling skills,
• good communication skills with sufficient proficiency in oral and written English,
• excellent study results,
• the ability to independently drive one's own research, but also to collaborate with others,
• a high level of creativity, thoroughness, and a structured approach to problem-solving,
• interest in contributing to the development of the research environment.

Additional qualifications 
Knowledge of social network analysis, knowledge of combinatorial optimisation methods, familiarity with social science scholarship, and experience with interdisciplinary research are valued.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date January 1, 2026 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Prof. Matteo Magnani, +46184714021, matteo.magnani@it.uu.se.

Please submit your application by December 10, 2025, UFV-PA 2025/3575.

Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden.