PhD Student in Electronic-Structure Machine Learning for Materials

• Contribute to the co-development of transferable e-ML models, investigating the interplay between model design, training strategies, computational efficiency, transferability, and predictive accuracy across a broad range of materials systems
• Generate and curate high-quality electronic-structure datasets using automated and reproducible AiiDA-based workflows for model training and benchmarking
• Validate and benchmark the predictive performance of the models for advanced materials properties beyond standard band structures and charge densities, including electron-phonon coupling and operators and observables related to Berry phases and other electronic-structure quantities
• Explore the development of transferable foundation models for materials applicable across the periodic table
• Contribute to the development of robust, reusable, and efficient open-source software and workflows, integrating machine-learning frameworks with established electronic-structure codes

Do you have experience in Research?, Do you have a Master's degree?, We are looking for a highly motivated candidate with a background in computational materials science or condensed-matter physics, and a keen interest in developing and applying advanced simulation methods and implementing them in workflows. You have experience working independently but also enjoy working in an interdisciplinary and collaborative environment and are eager to combine methodological development with real scientific applications. We do not expect candidates to be experts in all techniques at the start of the PhD; training and learning will be an integral part of the project. Requirements for candidates include:

• Master's degree (or close to completion) in physics, materials science, chemistry, engineering, or a closely related field
• Hands-on experience using density functional theory DFT for research or projects, and/or experience in the development of machine-learning ML models applied to materials
• Working knowledge of Python for scientific computing and data analysis
• Comfortable communicating research ideas and results in English, both in writing and in conversation
• Interest in quantum simulations, modern machine-learning models, the development of new computational methods, and/or materials modeling

Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure.