NLP & Embeddings Intern for Epidemic Intelligence

WOAH; Paris; On-site; EUR 7,800 - 8,900; Full time

Benefits offered by this job

Monthly stipend

WOAH HQ in Paris

Job summary

WOAH seeks an internship in its Data Science Lab (DS Lab) in Paris to develop and evaluate a semantic-matching pipeline for epidemic intelligence. You will review multilingual embedding models, build pipelines in Python or R, and compare results against reference descriptions of disease events.

The project involves collaboration with the EPIQ team and WOAH regional partners. Ideal candidates are enrolled in a Master’s program in data science or related fields with experience in text processing,

Qualifications

• Currently enrolled in a Master’s degree or final year of related program.
• Experience with Python or R for text data processing and statistics.
• Interest in NLP, embeddings, and multilingual processing is desirable.

Responsibilities

• Review embedding models for multilingual coverage and licensing.
• Build a reproducible semantic-matching pipeline in Python or R.
• Compare embeddings against disease-event references and open-source articles.
• Apply pipeline to EPIQ data and WAIS/open-source streams to assess added value.
• Document pipeline and methods using best practices and AI coding tools.
• Collaborate with the DIAD and WOAH teams and stakeholders.

Skills

Python

NLP

LLMs

AI coding tools

Statistics

English

Reproducible code

Git

Education

Master's degree in data science / related field

Tools

Claude Code

Codex

Git

Job description

WOAH seeks an internship in its Data Science Lab (DS Lab) in Paris to develop and evaluate a semantic-matching pipeline for epidemic intelligence. You will review multilingual embedding models, build pipelines in Python or R, and compare results against reference descriptions of disease events.

The project involves collaboration with the EPIQ team and WOAH regional partners. Ideal candidates are enrolled in a Master’s program in data science or related fields with experience in text processing,