PhD Position in Regulatory Learning, Participation, Evidence and Regulatory Memory at Technical University of Munich
Technical University of Munich (TUM) is recruiting a doctoral researcher for the MSCA Doctoral Network
REGULAIRE
(Regulatory Learning for the Governance of Transformative Technologies).
The project focuses on
technology governance
,
regulatory learning
,
public policy
,
participatory research
, and how institutions gather, document, reuse, and preserve evidence over time. This specific doctoral project examines how public agencies and other governance actors retain knowledge across regulatory cycles, with attention to participation by young people and other underrepresented groups.
The position is based at the
Technical University of Munich
, School of Social Sciences and Technology, and is supervised by
Prof. Sandra Cortesi
, with the network coordinated by
Prof. Urs Gasser
and Prof. Sandra Cortesi. The work is on site in
Munich, Germany
; teleworking is not permitted.
This is a
paid, full-time, 36-month employment contract
under MSCA rules, not a scholarship. The gross monthly salary is approximately
€3,800
before taxes and social security contributions, and the package includes social security, health insurance, pension contributions, MSCA mobility allowance, and possibly a family allowance.
Applicants should have a
Master’s degree or equivalent
by the start date and a background in law, political science, science and technology studies, psychology, communication science, information and library science, or another social science/interdisciplinary field. Strong English skills are required; German is not required. Applicants must also satisfy MSCA mobility and doctoral enrolment rules.
Helpful experience includes document or archival analysis, FOI requests, governance/process mapping, work in government or regulatory settings, and qualitative or mixed methods. The project may use case studies, interviews, participatory methods, document analysis, workshops, and analysis of public records.
Apply online by
31 October 2026, 00:00 CET
. Submit a CV, degree certificates and transcripts, a short research proposal (max 2 pages), and contact details for two referees.