Research focus

Generative Engine Optimization (GEO) is an emerging practice that aims to increase a brand’s visibility in content generated by AI models – much like SEO does for traditional search​. As AI-powered “answer engines” (e.g. ChatGPT, Google’s Search Generative Experience) gain popularity, users increasingly get direct, synthesized answers instead of just links​. This shift requires content creators and businesses to adapt.

Your tasks
Given the paradigm shift in marketing of searching products from search engine to direct recommendation from Chatbot based on Large Language Models (LLMs), we’d like to explore following topics:

• Which GEO-specific content features (e.g., citations, statistics, readability) most significantly drive generative engine recommendations?
• How do changes in content presentation affect the synthesized responses generated by AI-driven search engines? How does recommendation sentiment change with content presentation?
• Can we develop quantitative and qualitative metrics that accurately forecast content visibility in generative search outputs?
• What actionable guidelines can be established to help marketers integrate GEO techniques alongside traditional SEO strategies? What are the implications for marketing KPIs?
• …

 
The successful candidate will work closely within the team of Prof. Chunyang Chen and Prof. Christine Eckert to support all ongoing projects in this direction and will develop and employ novel machine learning-guided approaches.

Your qualifications

• Strong background in computer science, AI, or related areas or similar fields.
• Solid understanding of business and marketing concepts is essential.
• Good programming skills in at least one programming language e.g., Python.
• Experience with machine learning or LLM methods will be a plus.
• Enthusiasm to learn and develop new methods and techniques.
• Ability to conduct interdisciplinary research in an international collaborative environment.
• Proficient written and spoken skills in English and German.
• Participation in the supervision of university teaching, the teaching and research language is English.

We offer you

We offer you an exciting and challenging project within a dynamic and collaborative research environment. Access to rich and high-quality experimental data based on state-of-the-art technologies is ensured through our long-standing and internationally highly recognized experimental expertise in the field, but also through many close collaborations with other laboratories throughout the world.

Salary is paid according to remuneration group E13 TV-L (100%) of the pay scale for the German public sector. This is a position for PhD. TUM is an equal-opportunity employer. Therefore, women are especially encouraged to apply. We hope this position can start in 2025.

Applications
Complete applications should be sent to recruitment(at)seai.cit.tum.de. Please include a CV, a cover letter explaining why you are interested in the position and how you fit the profile, a brief summary of previous work experience and contact information of at least two referees.