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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.

2025

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
Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy

Abgaryan, M.; Cui, X.; Gopan, N.; della Maggiora Valdes, G. E.; Yakimovich, A.; et al. (6 authors)

A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning

De, T.; Thangamani, S.; Urbanski, A.; Yakimovich, A.

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