The Imperial AI Policy Fellowship 2027 is open for applications, and the deadline is Monday 2 November 2026. This 9-month fellowship is designed for senior civil and public servants who want to strengthen their understanding of AI in the public sector.
The program combines in-person sessions at Imperial College London with online learning and a fellow-led research project. In addition, fellows receive academic mentorship and engage with the Imperial Policy Forum and the wider Imperial community.
Application Deadline: 2 November 2026
Tell Me About The Award:
The Imperial AI Policy Fellowship is a professional learning and development programme for senior civil and public servants seeking to strengthen their understanding of artificial intelligence and its role and impact in the public sector.
The programme combines theoretical learning with practical application. Fellows explore AI concepts, public policy implications, strategy, regulation and delivery, while developing an individual research project focused on a policy challenge relevant to their work.
Which Fields are Eligible?
The Fellowship focuses on AI policy and public-sector applications of artificial intelligence, including:
- AI policy and regulation
- Public-sector AI deployment
- Strategic planning and technical solutions
- AI adoption within government departments
- AI systems and infrastructure
- Data storage and management
- AI research and experimentation
- AI-enabled transformation
- Public-sector AI safety and accountability
Research projects may include written recommendations, strategic or technical solutions, AI pilots, infrastructure improvements, workshops or published white papers.
Type:
Professional fellowship / policy learning and development programme.
Who Can Apply?
The programme is designed for senior civil and public servants.
Applicants should be seeking to strengthen their understanding of AI and apply that knowledge to policy challenges within the public sector.
The source does not provide a detailed list of additional eligibility requirements.
How Are Applicants Selected?
The source provides information about the application process but does not specify detailed selection criteria or a formal selection methodology.
Applicants are required to complete the application form and submit it to [email protected] by the application deadline.
Which Countries Are Eligible?
The source does not specify a list of eligible countries.
The programme is described as being designed for senior civil and public servants, with examples of previous Fellows and projects involving UK government departments.
Where Will the Award Be Taken?
The Fellowship is delivered in London and online.
In-person sessions take place at Imperial College London, alongside online engagement.
How Many Awards?
The source does not specify the number of Fellows selected for the 2027 cohort.
What Is the Benefit of the Award?
Fellows receive:
- A tailored learning experience based on their professional needs and interests.
- Practical application through an individual research project.
- Support from one or more academic mentors.
- Access to Imperial College London’s expertise in AI research and application.
- Networking and collaboration opportunities with other civil-service professionals.
- Opportunities to engage with the wider Imperial community.
- Exposure to AI concepts and their implications for public policy, strategy, regulation and delivery.
- Opportunities to explore live AI policy challenges relevant to their work.
How Long Will the Award Last?
The Fellowship lasts 9 months, from February to October 2027.
Fellows are expected to commit approximately 6–8 hours per month, as well as four in-person days throughout the year.
The programme fee is £1,500, with no VAT added.
How to Apply:
Applications for the 2027 Fellowship are open and close on Monday, 2 November 2026.
Applicants should complete the application form and send it to: [email protected]
The programme information also provides an application form and brochure for applicants to review before applying.
👉 Visit the Application Webpage for Details
