DTU Driven PhD candidate in Computational Social Sciences
Université du Luxembourg
Luxembourg, Luxembourg
il y a 15j

Research direction :

The global rise of collaborative research in science, technology, engineering, mathematics, and health (STEM+) fields continues to grow exponentially along with scientific production in aggregate.

This growth still needs to be mapped for many countries. Completed case studies of selected countries in Europe, North America, East Asia, and the Middle East show how research policies support the continued expansion of higher education and scientific production, but we know less about their specific support for international, intersectoral, and interdisciplinary collaborations.

Combining available big datasets (specifically the SPHERE project data based on Clarivate Analytics' Web of Science SCIE) with analysis of research policies and other factors will enable us to identify the drivers of scientific production and different forms of collaborations.

In organizational terms, throughout the world, universities are becoming ever more central to contemporary societies, as they vastly increase these countries’ capacity for science.

Governments and firms increasingly rely on university-based researchers to create new knowledge, certified by the peer-review process that guides publication of cutting-

edge research in thousands of scientific journals. However, the contributions of different organizational forms - from universities and research institutes to firms and government agencies -

as well as of individual organizations, demands in-depth analysis. The doctoral candidate would utilize and recode available data to delve below the global aggregates and uncover country, discipline, organizational form, and organizational trends and patterns by exploring network-

based methods and data mining techniques.

Supervision :

Your lead supervisor will be Prof. Andreas Zilian. Further supervision will be provided by Prof. Justin Powell, Dr. Jun Pang, and Prof. Stéphane Bordas.

Your primary tasks as a DRIVEN fellow are to :

  • Manage and drive forward your research project.
  • Attend doctoral school courses, trainings and social events.
  • Write scientific articles and your PhD thesis.
  • Disseminate your research at conferences and seminars.
  • Gender policy :

    UL strives to increase the proportion of female PhD students in its faculties. Therefore, we explicitly encourage women to apply.

    Your Profile

  • Master's degree in Computer Science, Computational Sciences, Computational Sociology or Social Sciences.
  • Profound knowledge of Data Science, Graph Analytics, Content and Metadata Analysis, Quantitative Empirical Social Research, Bibliometrics, Social Network Analyses.
  • Experience with the statistical analysis software STATA, SPSS or R
  • Good English language skills (a certified level B2 is the minimum required, while level C1 is a plus).
  • Willingness to work in an inter-cultural and international environment.
  • Ability to work independently and as part of a team.
  • Curiosity and self-motivation.
  • We offer

  • A dynamic and well-equipped research environment within the Department of Computational Engineering and Sciences
  • Intensive training in scientific and transferable skills, participation in schools. conferences and workshops.
  • Fixed term employee contract totalling 48 months, subject to review, at UL
  • Enrollment as a PhD student in the DRIVEN Training Unit, within an appropriate the UL doctoral school, e.g. Doctoral School of Science and Engineering
  • Personal work space at UL.
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