Project Background

A Master’s internship at Helmholtz Munich in the Lobentanzer Lab, building on ongoing project work with the German Center for Diabetes Research (DZD) and the Federal Institute of Public Health (BIÖG), sitting at the intersection of natural language processing and public health communication., * Design and build a structured dataset pipeline pairing source documents with automatically generated summaries.

• Run experiments across multiple language models and generation strategies.
• Investigate how and where information distortion occurs between source and generated text, and whether it can be detected systematically.
• Work with large language models, embedding models, and multi-agent pipelines.
• Build robust logging and experiment tracking infrastructure.

Prerequisites: Strong background in NLP and machine learning; solid Python skills; hands-on experience with LLMs (e.g., via HuggingFace or OpenRouter) and embedding models. Familiarity with evaluation methods for generated text or biomedical/clinical text is a plus.