AI@HZDR: 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

About us

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

AI LAB: Offers and Expertise

Our goal is to le­verage our expertise in AI for research, innovation, and collaboration with industry partners.
More
Foto: Icon AI consulting ©Copyright: iStock

Helmholtz AI Consultant Team

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

Mat­ter Research Area

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

Energy Research Area

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

Health Research Area

AI algorithms improve the diagnosis and treatment of cancer
More

AI Publications

Journal articles (refereed), Invited lectures

2024

Predicting the electronic structure of matter at scale with machine learning

Cangi, A.

  • Invited lecture (Conferences)
    Nano-Seminar, 17.10.2024, Dresden, Deutschland
    PURL: https://nano.tu-dresden.de/seminar/2024_10_17_attila-cangi

Bridging the gap in electronic structure calculations via machine learning

Cangi, A.


Towards Data-Driven Optimization of Experiments in Photon Science

Kelling, J.; Checkervarty, A.; Willmann, A.; Rustamov, J.; Aguilar, R. A.; et al. (6 authors)

  • Invited lecture (Conferences)
    Seminar at ELI Beamlines, 28.08.2024, Dolni Brezany, Czech Republic

Data science education in undergraduate physics: Lessons learned from a community of practice

Shah, K.; Butler, J.; Knaub, A. V.; Zenginoğlu, A.; Ratcliff, W.; et al. (6 authors)

  • Open Access Logo American Journal of Physics 92(2024)9, 655-662
    DOI: 10.1119/5.0203846
    arXiv: https://arxiv.org/abs/2403.00961

Improving Mineral Classification Using Multimodal Hyperspectral Point Cloud Data and Multi-Stream Neural Network

Rizaldy, A.; Afifi, A. J. M.; Ghamisi, P.; Gloaguen, R.


More publications