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:

• Development of modern machine learning methods
• Active contribution to the German-American research project “NeuroTronics”, with a particular focus on the design of doped semiconducting polymers using machine learning methods and self-driving laboratories
• Research in the field of autonomous decision-making methods based on Bayesian optimization techniques
• Collaboration with partners in the United States regarding their integration into automated synthesis and characterization workflows

In addition to scientific work, there is the opportunity of pursuing a PhD.

You have the following:

• 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 area of Bayesian optimization and/or self-driving labs is welcome
• Experience with quantum chemistry simulations of molecular or polymeric systems is welcome
• Experience with the development and training of large models or running simulations on high-performance computing systems is welcome
• Practical research experience and publications in relevant scientific areas are beneficial for the project.
• Experience in programming with Python, particularly with libraries such as PyTorch
• Fluent English language skills
• Strong communication and presentation skills