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Internship Programme last updated November 24, 2023 by Brian Pl├╝ss

Internship Programme

ARG-tech has a highly selective internship programme in which undergraduate students are funded to spend time working at the Centre.

We welcome students to work on research-relevant projects on a part-time basis.

Internships usually start with an initial period of 8 weeks which can be extended further based on successful progress. The workload is rather flexible, especially around deadlines and exams. Typically the workload is 2-10 hours per week during term time, but this can go up to 20 hours or full-time during holidays.

The specific work depends on the needs of ongoing research projects which host the intern at the Centre. Interns are supervised by the Internship Programme coordinator and the host project Principal Investigator.


Candidates should meet the following conditions to be eligible into the program:

  • Be currently enrolled in a relevant undergraduate programme at a UK University
  • Have the right to work in the UK
  • Typically spend a substantial part of the internship in Dundee

How to Apply

We accept applications to the internship programme at any time. Applications should be sent to Please include an up-to-date CV, and a cover letter describing why you would be a good fit for ARG-tech, and what skills you would bring to the team.

Ongoing Internships

  • Argument Technology for Fact-checking
    Tasks: developing a prototype front end for argument technology fake news identification
  • Argument Navigation Demonstrator
    Tasks: development of an argument navigation demonstrator for intelligence applications
  • Argument Technologies Infrastructure and Deployment
    Tasks: system administration for research infrastructure, and development of a deployment pipeline
  • Argument Analytics and Visualisation
    Tasks: Implementation of argument analytics algorithms and of a suite of interactive visualisations
  • Structural Search of Argument Data
    Tasks: Development of advance search functionalities to identify structural patterns in AIFdb