Two PhD Position (100%, 36 months) at The Copenhagen Center for Social Data Science (SODAS) at the University of Copenhagen (Denmark)

Copenhagen University
July 14, 2024
Offerd Salary:Negotiation
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Contract Type:Other
Working Time:Negotigation
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Who are we looking for? The Copenhagen Center for Social Data Science (SODAS; https: //, Faculty of Social Sciences, University of Copenhagen, invites applications for two PhD positions (100%, 36 months). The positions are funded by the Pioneer Center for Artificial Intelligence (P1) and the positions will be hosted at SODAS but affiliated with P1. Also, one position will be affiliated with DTU Compute and one position with the IT University of Copenhagen. The selected candidate will primarily work on modeling life- trajectory and network data in registry data and probing the resulting embeddings to understand biases. The work will be in close collaboration with the Nation-scale Social Networks project and with the Bias Explained project.

The positions are available from 1 October 2024, but a later starting date is also possible.

One selected candidate will be working closely with the research project Nation Scale Social Networks which investigates representations of social behavior and how predictive such representations are for life outcomes (e.g. education level, income wealth rank, unemployment history) based on registry data at Statistics Denmark. We are currently working on developing embeddings of life-event space, based on trajectories of life-events, using ideas from text embeddings. The other selected candidate will be working closely with the research project Bias Explained which focuses on quantifying bias in indicators and algorithms and create models to increase fairness in various contexts. Candidates can – but do not have to – indicate whether they have a preference to work closely with Nation Scale Social Networks or Bias Explained projects.

The PhD projects will focus on A. extending those ideas by identifying strategies for how to use network data to connect the individuals in the data and B. understanding how biases in the data can be detected in the underlying embeddings. In addition to these key objectives, the selected candidates are expected to collaborate with researchers from both computational and social sciences in an interdisciplinary environment, co- author scientific papers aimed at high-impact journals, and participate in international conferences. For the selected candidates, there will be possibilities to influence the project and develop new project ideas within the project frame. In addition, the chosen candidates will also be asked to fulfill other work obligations (typically teaching or supervising thesis students) to a small degree in the overall period.

Candidates should have a M.Sc. degree or equivalent.

  • You must have a background within Computer Science, Applied Mathematics, Physics, Social Data Science, or related fields.
  • Experience with machine learning, incl deep learning, and/or with complex networks
  • Experience with studying algorithmic bias and fairness is an advantage.
  • Experience with modeling complex networks or graph neural networks is an advantage.
  • Experiences using transformers is an advantage.
  • Experience working with Natural Language Processing is an advantage.
  • Experience with quantifying and modelling network inequalities is an advantage.
  • Experience analyzing event-sequences is an advantage.
  • Experience in time-series analysis is an advantage.
  • Experience with data visualization is an advantage.
  • An active interest in strong collaborations and interdisciplinary work is a plus.
  • An active interest in communicating science is a plus.
  • What do we offer? The chosen candidate will become part of the SODAS research environment and active doctoral program with about 20 PhD students. SODAS is an interdisciplinary research, teaching, and impact center, combining traditional social science theories and methods with novel data science methods and paradigms. In collaboration with partners in Denmark and abroad, SODAS is home to research projects funded by, among others, the European Research Council (ERC) as well as both independent and private Danish research foundations. SODAS hosts the English-language Social Data Science (SDS) Master degree program at the Faculty of Social Sciences.

    The working language at SODAS is English. Our community maintains strong collaborative relationships with all Departments at the Faculty of the Social Sciences (Anthropology, Economics, Political Science, Psychology, Sociology), the Department of Computer Science at the University of Copenhagen, as well as internationally renowned, computational social science research groups and centers in the Copenhagen area, including the Networks, Data, and Society (NERDS) group at the IT University of Copenhagen, the Social Complexity Lab at the Technical University of Denmark, and the Pioneer Centre for AI which operates across all Danish universities.

    How to apply Submit a complete application at our online portal. Click on the “Apply now” icon at the bottom of the page to apply. The documents must be in Adobe PDF or Word.

  • Cover/motivation letter (max. 3 pages)
  • Curriculum vitae (including publications and conference presentations, if applicable)
  • Diplomas and transcript of grades (bachelor and master, if applicable)
  • An abstract of the master thesis written in English (1 page)
  • Master thesis (if applicable)
  • Up to 3 research articles and/or working papers (if applicable)
  • Please note that it is only possible to upload one document per attachment category. If more than one document has to be uploaded in the same category, please make sure that they are scanned and collected into one file.

    Terms of Employment

  • In order to be awarded a PhD scholarship the chosen candidates have to enroll as PhD students at the Faculty of Social Sciences, cf. the rules of the Danish Ministerial order No 1039 of 27 August 2013. Most likely, the applicants will be enrolled in the PhD program at Center for Social Data Science, University of Copenhagen.

  • Further information about the PhD study programme is available on the website of Copenhagen Graduate School of Social Sciences: https: //

  • Under "Legal basis" on the website you will find information about the rules and guidelines for the PhD programme, and the Danish Ministerial Order on the PhD Programme at the Universities.

  • The exact salary depends on various aspects including the chosen pension scheme and taxation, but can be expected to be around DKK 21,000 net/month

  • The Recruitment Process Shortlisted applicants are notified of the composition of the assessment committee, and each applicant has the opportunity to comment on the part of the assessment that relates to the applicant themselves. Further information on these recruitment process at University of Copenhagen can be found here: https: //

    An Equal Opportunity Workplace The University of Copenhagen is committed in its pursuit of academic excellence to equality of opportunity and to creating an inclusive working environment and therefore encourages all qualified candidates to apply, regardless of personal background, gender, sexual orientation, age, disability, ethnicity etc. For more on the diverse working place environment at the University and the University's participation in the HRS4R HR Excellence in Research, see https: // charter-for-researchers/

    International Applicants The University of Copenhagen offers information and services for international researchers. Please find more information about these services as well as information on entering and working in Denmark here: https: //

    Contact Information Information about the recruitment process is available from HR, e-mail: [email protected] refer to ID number: 211-1868/24-2H #1

    Additional information about the position can be obtained from Sune Lehmann ([email protected]).

    The closing date for applications is 14 July 2024 at 23:59CEST. Applications or material received after the deadline will not be taken into account.

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