Internship – Fine-tuning Large Language Models for Contextualised Outputs

• Work with WOAH editors/linguists to gather glossaries, style guides, and a representative set of prior translations and editorial corrections
• Define the target tasks and acceptance criteria
• Review and compare adaptation approaches
• Propose two to three candidate strategies for different operational contexts
• Prepare training/validation datasets in the formats required by the selected fine-tuning platforms
• Implement reproducible scripts (R and/or Python) to perform tokenisation estimates, convert formats, and upload or launch fine-tuning jobs
• Conduct controlled fine-tuning/adaptation experiments on at least two approaches
• Evaluate using quantitative metrics and qualitative review by WOAH editors
• Document expected cloud fine-tuning costs based on WOAH datasets
• Analyse operational implications
• Deliver clear step-by-step pipelines, annotated code, and operational instructions for DID staff
• Produce final technical report, reproducible notebooks, and an executive summary for non-technical stakeholders
• Develop a prototype graphical interface (in Dash or RShiny) to allow WOAH staff to upload small samples of text for testing, select between models, provide glossary terms or contextual examples, view side-by-side outputs for editorial evaluation
• Ensure that the GUI is lightweight, easy to deploy locally, and documented

• Master level (or equivalent) in data science. Alternatively in veterinary sciences, life science, international affairs, public health, agricultural science with formal training or experience in data science
• R or python advanced level
• Familiarity with LLM fine tuning theory and methods would be a plus
• Analysis and problem solving
• Good working knowledge of Microsoft Office, such as Excel, Word and PowerPoint
• Excellent knowledge of English, both spoken and written
• Basic knowledge of epidemiology and epidemic intelligence principles
• Ability to work in an agile way and deliver at pace… organisation skills and ability to meet deadlines
• Team player who can integrate well into the department and is willing to commit to supporting the development of Observatory outputs

Familiarity with LLM fine tuning theory and methods would be a plus