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

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

11.08.2025 AI Lab: New Helmholtz AI Project Call 2025 was opened, deadline is Nov. 13th.

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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

2026

Gas bubble detection and segmentation using a machine learning approach leveraging semi-supervised training

Schäfer, J.; Taş, S.; Hampel, U.

Denoising, Deblurring, and Optical Deconvolution for Microscopy with a Physics-informed Deep Neural Network DeBCR

Li, R.; Yushkevich, A.; Chu, X.; Kudryashev, M.; Yakimovich, A.

2025

Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

Arbash, E.; Afifi, A. J. M.; Belahsen, Y.; Fuchs, M.; Ghamisi, P.; et al. (7 authors)

Foundational Models in physics and its neighborhood

Steinbach, P.; Schmerler, S.

  • Open Access Logo Invited lecture (Conferences)
    11. Annual MT Meeting, 03.-06.11.2025, Darmstadt, Germany

Tutorial on Scalable Machine Learning for Electronic Structure Calculations

Cangi, A.

  • Invited lecture (Conferences)
    Scientific symposium and autumn school on „Chemistry, Physics & Devices of Organic 2D Crystals", 06.-10.10.2025, Dresden, Deutschland

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