The basic outline of the programme involves theoretical courses in the first year, while the second year is dedicated to your own research. The study programme comprises a total of 120 EC. 

Curriculum 

The programme includes the following components, with the specified credit load:

  • Compulsory courses (22.5 EC)
  • Primary electives (at least 30 EC)
  • Secondary elective (7.5 EC)
  • Master’s thesis (37.5 or 52.5 EC)
  • Study abroad or internship (optional, 15 EC)
  • Research Seminar (7.5 EC)

Your second year will be dedicated to a research project, optionally combined with a internship, that is closely attuned to current developments in your chosen field and culminates in your Master’s thesis.

History and Philosophy of Science study programme
EC = credits (European Credit Transfer and Accumulation System)

Examination Methods 

  • Papers
  • Presentations
  • Assignments
  • Master’s thesis: Your Master’s thesis is a reflection of your training as a researcher in HPS. It should be written in  English. At the end of your project, you will  a formal presentation about your work in the colloquium series of  the Descartes Centre or the Freudental Institute.

Educational profile - teacher degree

A profile is a coherent set of courses totaling 30 EC and can be chosen to expand the thematic range of your Master's programme. The courses are on a single theme that is usually not a standard part of the programme.

If you are passionate about sharing your knowledge, and you would consider a career as a teacher in secondary education, this profile might be right for you. The emphasis of the Educational Profile is on practitioner skills and school-based activities. Throughout the profile, learning theories and teaching methods will be taught closely linked to your day to day work in the classroom. The profile is tailored to meet the professional development needs of teachers in the early stages of their careers. More information.

Applied Data Science profile

Applied Data Science is a multidisciplinary profile for students who want to better understand and be able to apply Data Science methods, techniques, and processes to solve real-world problems in various application domains. The foundations of applied data science include relevant statistical methods, machine learning techniques and script programming. Moreover, key aspects and implications of ethics, privacy and law are covered as well. Data science methods and techniques are investigated using case studies and applications throughout the life sciences & health, social sciences, geosciences, and the humanities. More information.