Job description
As a successful candidate, you will work on the development of machine learning methods in the AI for Materials Science (AiMat) group of Prof. Dr. Pascal Friederich.
Your responsibilities will include:
1. Developing modern machine learning methods that open up new avenues for analysing and designing molecular systems
2. Actively shaping collaboration in the DFG Priority Programme ‘Molecular Machine Learning’, in particular with the experimental partners of the project ‘Design of photocatalytic systems for CO₂ reduction through synergistic collaboration between machine learning and automated laboratories’ at KIT
3. Researching and further developing self-explanatory graphical neural networks for molecules and integrating them into automated synthesis and characterisation workflows in order to efficiently advance data-driven research
4. Cooperating with the automated synthesis laboratories at the Institute of Organic Chemistry In addition to scientific work, there is the opportunity of pursuing a PhD.
Starting date
01.02.2026
Personal qualification
5. A Master’s degree in Computer Science or a Natural Science from an internationally recognized academic institution.
6. Theoretical and practical experience in the fields of machine learning and deep learning
7. Experience in applying machine learning methods in an interdisciplinary context is an advantage
8. Experience in the field of explainable AI and/or graphical neural networks and/or self-driving laboratories is an advantage
9. Experience in the development and training of large models or the execution of simulations on high-performance computing systems is desirable
10. Ideally, practical research experience and experience with publications in relevant scientific fields
11. Fluent English skills
12. Strong communication and presentation skills