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 machine learning methods in collaboration with partners of the DFG Excellence Cluster “3D Matter Made to Order,” with a focus on ML potentials for complex atomistic simulations of laser 3D printing processes
• Research in the areas of accelerated atomistic simulations, universal ML potentials, and model distillation
• Collaboration with experimental partners in the 3DMM2O project to apply and validate the developed ML potentials
• Preparation of scientific manuscripts and presentation of research results at workshops and conferences

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 machine-learned potentials and/or molecular dynamics simulations and/or density functional theory is an advantage
• Experience in developing and training large models or performing simulations on high-performance computing systems is desirable
• Ideally, practical research experience as well as experience with publications in relevant scientific fields
• Experience in programming with Python, especially with libraries such as PyTorch
• Fluent English skills
• Strong communication and presentation skills