The Department of Economics at Ghent University is inviting application for a PhD position. The position entails applying Bayesian statistical techniques to develop models of decision making, and to the analysis of field and laboratory data on decision making, oftentimes in developing country settings.

  • Candidates should hold a master degree in Statistics, Mathematics, or Computer Engineering. A two-year research master is an asset.
  • Applicant should be familiar with Bayesian (hierarchical) modelling, and should have an interest in applying these methods to questions in economics and decision making.
  • The master’s degree should be recent (graduated no more than 2 years ago), and obtained from a European University (in exceptional cases degrees from non-European universities supplemented by a GRE test may be acceptable).
  • Candidates with a master degree in economics or psychology may be considered if they can show a strong quantitative background and advanced knowledge of Bayesian statistics.
  • Familiarity with software packages R and Stan are highly desirable.
  • Familiarity with cloud computing and working with server clusters is an asset. The position is research focused. The working language is English.

How to apply:

  • Applications should be sent as one pdf-document to Prof. Ferdinand Vieider ( with reference “PhD student in Bayesian Statistics”, and should include the following:
  • Cover letter
  • CV
  • Details of the master courses taken (including transcripts, grades, and ideally comparative rankings).
  • Reference letters are not required at the initial stage of the application, but candidates should indicate two letter-writers who can be contacted in case they get selected for an interview. Reference letters should be sent directly to
—————–Quick Overview————-
Organization Ghent University
Country Belgium
Fellowship Level Doctoral
Subject areas Economics, Statistics, Mathematics, Computer Engineering
Fellowship amount Varies
Eligibility Open to all nationalities
Deadline Aug 31, 2021

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