DC10 / Institutional capacity
Frugal Regulation: Learning Loops for Low Resource Regulators
Short description
Regulators with limited budgets still need to keep pace with complex technological change. This project asks how they can build useful, lasting learning processes using affordable tools and collaboration.
Through case studies of AI policy, the researcher will examine resource constraints and existing cooperation. They will co-design low-cost feedback protocols, open tools and cross-agency exchanges that help regulatory knowledge travel across regions.
Recruitment information
Interested in this project?
Recruitment is planned for autumn 2026. The official university vacancy will be linked here when it is published, with the application procedure, deadline and employment conditions.
Job advert coming soonApply through the host university’s official recruitment procedure. Read the applicant FAQ.
What the project involves
- Map capacity constraints and collaboration patterns through AI policy case studies.
- Co-design low-cost feedback protocols, open tools and cross-agency fellowship approaches.
- Develop and field-test mechanisms for cross-regional knowledge exchange and contribute open resources.