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# Master's Thesis - LLM-Based Narrative Virality Analysis and Signal Design in Economic and Market Text

- **Company:** Siemens Energy
- **Location:** Erlangen, Germany
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Text Mining, Data Processing, Large Language Models, Information Technology, Data Analytics, Text Analysis
- **Published:** June 5, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=a0d0ee593c277a83

## About the Role

Do you have experience in Research?, Do you have a Master's degree?, * Studies in Data Science, Computer Science, Artificial Intelligence, or a related field at Master's level

 * Knowledge of Python-based data processing , NLP, or text-mining workflows for text analysis and prototyping
 * Interest in large language models, prompt-based methods, narrative analysis, and information diffusion in economic, business, or market-related settings
 * Understanding of clustering, similarity analysis, or time-based signal construction for text-driven indicators
 * Ability to work independently on implementation, analysis, and technical documentation in an applied research environment
 * Experience with embeddings, public text datasets, event analysis, or experimental evaluation as an advantage

## Description

You work with public economic and market-related text and investigate how narratives spread across sources and time. Your focus is on exploring LLM-based methods for measuring narrative spread, salience, and persistence, and on transforming these dynamics into structured and interpretable signals.
How You'll Make an Impact

 * Development of a research framework for measuring narrative spread and salience in public economic and market-related text
 * Exploration of LLM-based and clustering-based methods for identifying coherent narrative themes across sources and time
 * Implementation of a prototype for virality-oriented or spread-based signal generation from textual data
 * Evaluation of different dimensions of narrative spread, such as speed, source diversity, persistence, and cross-source propagation
 * Documentation of methodology, findings, and limitations for future reuse in applied text and data analytics
 * Support with the translation of research outcomes into interpretable signal concepts for forecasting-related use cases


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