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: 

• Developing modern machine learning methods that open up new avenues for analysing and designing molecular systems
• 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
• 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
• 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.

• A Master’s degree in Computer Science or a Natural Science from an internationally recognized academic institution.
• Theoretical and practical experience in the fields of machine learning and deep learning
• Experience in applying machine learning methods in an interdisciplinary context is an advantage
• Experience in the field of explainable AI and/or graphical neural networks and/or self-driving laboratories is an advantage
• Experience in the development and training of large models or the execution of simulations on high-performance computing systems is desirable
• Ideally, practical research experience and experience with publications in relevant scientific fields
• Fluent English skills
• Strong communication and presentation skills