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.

News from the AI Lab

15.09.2025 AI Lab: AI Lab at Building Bridges 2025

03.09.2025 AI Lab: New AI Courses offered by the Helmholtz Academy

02.09.2025 AI Lab: HZDR AI Symposium on Tuesday, September 9th, 9 a.m. till 4 p.m. in the lecture hall

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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

2025

HyperPointFormer: Multimodal Fusion in 3-D Space With Dual-Branch Cross-Attention Transformers

Rizaldy, A.; Gloaguen, R.; Fassnacht, F. E.; Ghamisi, P.

  • Open Access Logo IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18(2025), 21254-21274
    DOI: 10.1109/JSTARS.2025.3595648

Large Language Models from User-Interface to Transformers

Steinbach, P.

PET und KI: Ein narrativer Überblick

Rogasch, J. M. M.; Mikolajczak, J.; Hellwig, D.; Maus, J.; Hofheinz, F.; et al. (7 authors)

Data management makes machine learning easier

Korten, T.; Hoffmann, H.; Steinbach, P.; Özkan, Ö.

Hydrogen adsorption energy trends in Mo/WXY (X, Y = S, Se, Te) regular and Janus TMD monolayers: A first-principles and machine learning study

Tejaswini, G.; Sudheer, A. E.; Kumar, A.; Perepu, P. K.; Vallinayagam, M.; et al. (10 authors)

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