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Materials Science and AI: First PhD Summer School at Aschaffenburg UAS

Teilnehmende der PHD Summer School in Aschaffenburg

International doctoral students exchange ideas on machine learning in materials science.

From July 22 to 24, the first PhD Summer School on Machine Learning for Research in Materials Science (ML4MatSci) took place at the Aschaffenburg University of Applied Sciences. It was organized by the European network EuMINe (European Materials Informatics Network) and the joint Doctoral Center for Sustainable and Intelligent Systems (NISys) of the Aschaffenburg UAS, the Coburg University of Applied Sciences and Art, and the Technical University of Applied Sciences Würzburg-Schweinfurt.

The three-day event brought together doctoral students from the EU as well as associated countries such as the Western Balkans and Israel, who are conducting research in the field of materials science. The aim of the summer school was to familiarize young scientists with methods and applications of machine learning and to encourage them to use these methods in their materials science research projects.

The summer school was organized by Prof. Dr. Michael Möckel, professor at the Faculty of Engineering and Computer Science (UAS Aschaffenburg), in cooperation with Prof. Dr. Amila Akagic (University of Sarajevo) and the head of the EUMINE network, Prof. Dr. Francesco Mercuri (CNR Bologna). As the local host, the NISys doctoral center held the one-day event at the TH Würzburg-Schweinfurt, giving guests a broader view of the university landscape in the Bavarian Lower Main region.

  • Participants of the PhD Summer School

    Participants of the PhD Summer School

  • Speakers at the PhD Summer School

    From left to right: Prof. Dr. Michael Moeckel, UAS Aschaffenburg, Germany (local organizer), Prof. Dr. Amila Akagić, University of Sarajevo, Bosnia-Herzegovina (co-organizer), Medina Kapo, University of Sarajevo, Bosnia-Herzegovina (Teaching Assistant), Kuniko Paxton, University of Hull, UK (Teaching Assistant), Jorrit Voigt, THAB, TU Braunschweig & VW AG, Germany (Industry Representative)

An interdisciplinary program combining theory, practice, and exchange

The summer school program combined theoretical lectures by international experts with practical, hands-on teaching units. In poster sessions and short pitch presentations, participants were able to present their own research projects and receive valuable feedback. Numerous doctoral candidates from the NISys doctoral center also took the opportunity to present their research.

The use of machine learning methods in materials science is highly dynamic and has already yielded spectacular results. The Summer School therefore focused on generative models for the targeted development of new materials and molecules, Bayesian optimization approaches for the efficient acquisition of measurement data, the calibration of machine learning methods, and applications in medical imaging and industry.

An evening excursion to the Würzburg Residence gave participants an insight into the materials science aspects of the conservation and reconstruction of historical monuments and highlighted the potential for the use of AI methods in this field.

The summer school was funded by the European network EuMINe as part of the EU-COST (European Cooperation in Science and Technology) initiative with funding from the European Union. It both enabled scientific exchange and promoted networking among young scientists across disciplines and national borders.