Summary
The Kuenneth Group (Computational Materials Sciences) at the University of Bayreuth is seeking a highly motivated Postdoctoral Researcher for the ERC Starting Grant project on generative polymer informatics (genPI). This role focuses on bridging the gap between large language models (LLMs) and chemical knowledge to advance polymer design.
| Designation | Postdoctoral Researcher |
| Research Area | Materials Informatics / Computational Materials Sciences |
| Location | Bayreuth, Germany |
| Salary Scale | TV-L E13 (100%) |
| Last Date for Apply | Until Filled |
Job Description
The successful candidate will contribute to the genPI project through the following responsibilities:
- Build and evaluate retrieval-augmented generation (RAG) systems connecting LLMs with chemical knowledge.
- Develop agentic AI workflows to support innovative polymer design.
- Extract, curate, and organize polymer and synthesis data to ensure quality and reproducibility.
- Conduct independent research, publish findings in high-impact journals, and collaborate with interdisciplinary partners.
Eligibility/Qualification
Applicants must meet the following criteria:
- A PhD in polymer science, chemistry, materials science, computer science, or a related field.
- Solid knowledge of polymer chemistry and polymer structure–property relationships.
- Strong Python programming skills and practical experience in applying machine learning or AI to scientific problems.
- Familiarity with LLMs and RAG concepts; experience with agentic AI and multi-step workflows is highly desirable.
- An independent, creative, and proactive approach to research with strong communication skills in English or German.
What We Offer
- A full-time position paid according to TV-L E13 standards.
- Flexible working hours with the option for home office.
- A pleasant working atmosphere with regular social events.
- A family-friendly environment (member of the “Familie in der Hochschule e.V.” club).
How to Apply
Interested candidates should submit their application documents online via the University of Bayreuth application portal. Important: You must use the password “GenP120 27” in your application.
For further inquiries, please contact: Prof. Christopher Künneth at apply.mi@uni-bayreuth.de.
Apply Link
Applications must be submitted through the official portal: University of Bayreuth Job Vacancies
