Artificial Intelligence Research Platform

Data scientists and researchers from diverse fields at HZDR design and train artificial intelligence models using increasingly large and cross-disciplinary datasets. This approach enables them to develop comprehensive solutions and uncover new insights and applications in the research fields of Matter, Energy, and Health.
The newly established HZDR AI Lab, founded in 2024, serves as a central hub for researchers within HZDR, at other Helmholtz centers, research institutions, and industry.

Foto: Materials of the future thanks to faster simulations, KI@HZDR ©Copyright: Blaurock/HZDR

AI Lab

Our AI Lab is the central hub for AI-related topics at the HZDR.
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Foto: Icon AI offers ©Copyright: iStock

Industry Collaborations

Our goal is to le­verage our expertise in AI for research, innovation, and collaboration with industry partners.
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Foto: Helmholtz AI Logo ©Copyright: Helmholtz AI

Helmholtz AI Consultant Team

We develop and disseminate AI-supported data science solutions to tackle the grand challenges in research and society.
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Foto: Brain-inspired computing ©Copyright: Blaurock Markenkommunikation/HZDR

Research Field Mat­ter

Particle accelerators, future materials, brain-inspired computing, planetary research
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Foto: Artificial intelligence supports the search for hard-to-reach mineral resources ©Copyright: HZDR/Blaurock Markenkommunikation

Research Field Energy

Machine learning methods drive the search for raw materials
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Foto: Cancer irradiation with the highest precision ©Copyright: Blaurock Markenkommunikation/HZDR

Research Field Health

AI algorithms improve the diagnosis and treatment of cancer
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AI Publications

Journal articles (refereed), Invited lectures

2026

Machine learning-based prediction of heavy metal leaching efficiency from zinc metallurgical waste

Mondal, P. P.; Panday, H.; Guy, B. M.; Dhiman, S.; Ahammad, S. Z.; et al. (6 authors)

When Do Machine Learning Models Learn Chemistry? From Electronic Structure to Latent Space Steering

Cangi, A.

  • Invited lecture (Conferences)
    RTG 2681 On-site Meeting, 12.05.2026, FAU Erlangen-Nürnberg, Deutschland

LANTERN-XGB: An Interpretable Multi-Modal Machine Learning for Improving Clinical Decision-Making in Lung Cancer

Dalfovo, D.; Sassorossi, C.; de Paolis, E.; Campanella, A.; Nachira, D.; et al. (17 authors)

Artificial intelligence - how does it work

Steinbach, P.; Korten, T.

  • Open Access Logo Invited lecture (Conferences)
    17th DRESDEN SYMPOSIUM on Feto-neonatal Health, 11.-13.03.2026, Dresden, Germany
    DOI: 10.6084/m9.figshare.31653523

Roadmap on deep learning for microscopy

Yakimovich, A.; Hecht, M.

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