Job Description
Are you excited about research in machine learning, in particular graph neural networks and/or generative models? Do you want to extend your knowledge to other fields of expertise in an interdisciplinary setting? Would you like to see how your research makes an impact on real-world applications in materials science and chemistry?
Yes? Then apply now for a PhD position in the AIMat research group at KIT!

What we offer

• Fully funded PhD position (TV-L E13)
• Young and ambitious research group with a very collaborative and international team of PhD students and Postdocs
• Exciting research topics, state-of-the-art machine learning methods, applied to highly relevant challenges in chemistry and materials science
• National and international collaborations in machine learning, materials science and chemistry
• Support through the Karlsruhe House of Young Scientists (KHYS), including workshops and financial support for stays abroad

Tasks

• You will be able to work on the development of machine learning models, in particular
  • Graph neural networks for property prediction of materials and molecules (deep graph represention learning models on very large datasets)
  • Generative models for inverse design (graph generation, e.g. based on diffusion)
  • Explainable methods for extraction of scientific knowledge from data, specifically graph datasets
• You will have the chance to participate in teaching activities on the Bachelor's and Master's level in computer science (German or English)
• You will have the opportunity to present your research on international conferences, project meetings and workshops
   

What we are looking for

• Highly motivated young researchers (female/male/diverse)
• Background and practical experience in computer science and machine learning, interest in graph neural networks and generative models
• Interest to learn more about exciting application areas of machine learning in materials science and chemistry
• Creativity, independence, good teamwork and communication skills, motivation to make a change and have an impact in research