Graduate Researcher / PhD Candidate (m/f/d) in materials informatics with the possibility of a doctorate TV-L E13, 100%
The Kuenneth Group (Computational Materials Sciences)
About the Project & Group The Kuenneth Group (Computational Materials Sciences) is seeking a Graduate Researcher / PhD Candidate (m/f/d) to join an EU Horizon Europe research project on using AI to design sustainable polymeric materials for electronics and packaging.
Your Responsibilities
- Managing the extraction, curation, and structuring of data to facilitate machine learning model training
- Building and implementing machine learning models, large language models (LLMs), and autonomous AI agents tailored for polymer research
- Bridging the gap between machine learning frameworks and experimental workflows
- Performing autonomous research within a fast-paced, creative scientific setting
Your Profile
- An above-average master's degree in Engineering, Computer Science, Natural Sciences, or related fields
- Strong programming proficiency in Python is required
- Knowledge in polymer and materials science
- You possess curiosity towards materials and data science, along with creativity and independence
- Motivation and proactivity, with strong communication skills in English or German
What We Offer
- A full-time position paid according to TV-L E13 standards for up to 3.5 years
- Flexibility in working hours with the option to work from home within the framework of the applicable service agreement
- The opportunity to pursue a doctoral degree (PhD) while accessing a wide range of industrial and international contacts
- A pleasant working atmosphere with social events
The University of Bayreuth is a member of the Best Practice Club "Familie in der Hochschule e.V.". We strongly encourage women to apply and welcome applicants with children. Persons with severe disabilities will be given preferential consideration if equally qualified.
How to Apply
Please submit your meaningful application documents online via the application portal of the University of Bayreuth.
- Required Password: You must use the password "MI2026" in your application
The documents will be deleted after the position has been filled in accordance with data protection requirements.
For further inquiries please contact: Prof. Christopher Künneth (apply.mi@uni-bayreuth.de)