AI Policy Lab Externships 2026 at Umeå University, Sweden
- Omran Aburayya
- Sep 10
- 3 min read
Updated: Sep 11
If you’re a Bachelor’s or Master’s student—or even a recent graduate—with a strong research interest in AI and society, the AI Policy Lab Externship Program 2026 at Umeå University is now accepting your application. From January 1 to June 30, 2026, join a globally diverse cohort dedicated to exploring key challenges at the intersection of AI, governance, human rights, sustainability, education, and more . This flexible, part-time externship offers a unique platform to contribute to real-world policy research while collaborating with a vibrant international research community. Read on for details.
🎓 Externship Summary
- Location: Primarily remote; optional on-site participation at Umeå University if feasible 
- Host Institution: AI Policy Lab, Department of Computing Science, Umeå University, led by Wallenberg Scholar Prof. Dr. Virginia Dignum 
- Level: Bachelor’s & Master’s students, and recent graduates 
- Target Group: Students in disciplines such as Computer Science, AI, Philosophy, Political Science, STS, Law, or related interdisciplinary fields 
- Fields of Focus: - AI Governance & Compliance 
- AI and Human Rights 
- AI, Society & Democracy 
- Environmental Impacts of AI 
- Global Coordination & Policy Harmonization 
- AI and Education 
 
- Duration: Six months (January 1 – June 30, 2026) 
- Application Deadline: 17 October 2025, 17:00 CET 
- Start Date: January 1, 2026 
- Eligible To: Global applicants with requisite academic background and strong interest in Responsible AI 
📚 Externship Overview
Though named “externships,” this program functions much like a research internship. Externs will:
- Conduct literature reviews and background research 
- Participate in workshops and short research training sessions 
- Contribute in group discussions and Lab seminars 
- Undertake independent, mentor-supported research projects 
- Deliver a final research output—such as a policy brief, white paper, or prototype 
This unpaid, part-time program is tailored to fit around academic commitments .
🎁 Benefits
- One-on-one mentorship with researchers at the AI Policy Lab 
- Certificate of completion recognizing your contribution 
- Opportunities to publish or present work via Lab channels 
- Hands-on experience in AI policy research methods 
- Networking and feedback opportunities within an international research community 
✅ Eligibility Criteria
Applicants should:
- Be Bachelor’s or Master’s students—or recent graduates—in relevant disciplines 
- Demonstrate strong interest in Responsible AI and its societal implications 
- Show familiarity with AI governance and policy debates 
- Possess excellent writing, communication, and analytical skills 
- Exhibit a collaborative attitude and openness to working in an international team 
- (Optional) Have experience conducting research and literature reviews 
📝 Application Procedure
- Application: Online via the official portal 
- Deadline: 17 October 2025, 17:00 CET 
- Results Announcement: Mid-November 2025 
- Program Dates: January 1 – June 30, 2026 
- Format: Remote-first with optional on-site participation (travel/accommodation at extern’s cost) 
- Information Session: Scheduled for mid-September 2025 (date to be confirmed) 
📂 Documents to submit:
- CV (max 2 pages, highlighting results and practical deliverables) 
- Academic transcript (unofficial accepted) 
🔍 Selection will be based on motivation, topic relevance, and the ability to contribute, with emphasis on diversity in background and geography.
🚀 Why This Externship Matters
Joining the AI Policy Lab Externship means engaging at the frontier of responsible AI debates. Led by Prof. Virginia Dignum—a globally recognized figure in responsible AI and advisor to bodies like UNESCO and the UN AI Council—the Lab is an interdisciplinary hub committed to shaping AI governance that serves societal values . Your research will contribute to shaping policy, informed by cutting-edge discourse and delivered via collaborative, real-world methods.



