NetHope Humanitarian AI Research Fellowship 2026/2027

Deadline: October 9, 2026

Applications are invited for the NetHope Humanitarian AI Research Fellowship 2026/2027. NetHope is piloting a new, short-term Humanitarian AI Research Fellowship to grow the pool of researchers and practitioners who understand how AI can responsibly advance humanitarian and development work, and to produce research findings that NetHope’s Member organizations and the wider nonprofit community can put to immediate, practical use.

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This is a part-time, remote, internationally open opportunity for individuals who bring rigor, curiosity, and a genuine interest in responsible AI for humanitarian impact.

Benefits

• Fellows receive a stipend in recognition of their time and contributions. Details will be shared with selected fellows and set out in the fellowship agreement.

Eligibility

They welcome applicants from mid-career to seasoned practitioners who bring strong writing and analytical
skills and a genuine interest in responsible AI’s role in humanitarian impact.

• Stream 1 (Adoption & Governance): background in policy, program evaluation, or humanitarian/international development systems; experience with technology adoption or organizational change is a plus.
• Stream 2 (AI Systems & Localization): background in computer science, machine learning, or applied data science, with knowledge of or interest in the humanitarian sector. Experience in model evaluation and/or red teaming a plus.
• Across both streams: strong written communication, self-direction, and the ability to sustain an independent research project on a fixed timeline.

Application

Your application should include:

• CV
• Letter of interest responding to the following questions:
  • Tell them about your motivations, relevant experience, and interest in working in the humanitarian and nonprofit sector.
  • In your view, what is one of the top concerns or risks associated with AI in the humanitarian sector, and why? Briefly describe one possible way to mitigate it.
  • Think of an AI tool (open-source, commercial, or in-house) you’ve used or evaluated in a real project, not a demo. What was one way it under-performed for the actual context it was used in, and how did you find out? Don’t include confidential information about your employer or clients.
• Two to three professional references
• Optional: Writing sample

Apply: https://nethope.app.box.com/f/0377bdadf36e4b74b2f244fe07cd9f9b