Data Science, Social Innovation and Special Projects Team

Overview

This internship focuses on applied research at the intersection of multilingual NLP, cross-cultural AI alignment, and field-deployable computing within the Social Innovation and Special Projects Team.

---

Key Responsibilities

• Support the design of a portable compute platform for field data collection.
• Document hardware-software interface specifications and data schemas.
• Explore applications relevant to humanitarian response, environmental monitoring, and community health.
• Contribute to the design of data pipelines, storage schemas, and governance protocols.
• Prototype intelligent document retrieval and automated reporting capabilities.
• Apply principles of inclusion, integrity, safeguards, and sovereignty in digital public infrastructure design.
• Help establish reproducible engineering practices and prepare handover materials.
• Research and develop approaches for multilingual language understanding and translation in low-resource peacekeeping contexts.
• Design and execute participatory methods for eliciting culturally specific norms, values, and linguistic patterns.
• Build and curate datasets that capture cultural and linguistic variation.
• Develop alignment and evaluation methodologies that account for cultural pluralism.
• Contribute to a research paper documenting methods, experiments, and findings.

Required Experience

Applicants to the UN Internship Programme are not required to have professional work experience. Experience with programming skills in Python, R or an equivalent programming language. Familiar with model fine-tuning, evaluation design, and alignment techniques. Experience with participatory or community-centered research methods is an asset. Experience with research experience in NLP, multilingual language modelling, or cross-lingual transfer.

Qualifications

Applicants must meet one of the following requirements: (a) be enrolled in, or have completed, a graduate school programme (second university degree or equivalent, or higher). (b) be enrolled in, or have completed, the final academic year of a first university degree programme (minimum bachelor's degree or equivalent). Preferred fields include computer science, computational linguistics, machine learning, computational social science, or a related discipline. Candidates from social-science backgrounds (international relations, anthropology, linguistics) with strong computational skills will also be considered.

Other Details