Our network
From the Helmholtz Incubator to international labs across the globe, Helmholtz AI shares bonds with a robust network of scientific platforms and institutions.
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Local units
We are structured as a hub-and-spoke model with six units across the Helmholtz Association and we are built around our core themes of research, support and coordination & outreach.
Our headquarter is hosted by Helmholtz Munich and we have local units at Karlsruhe Institute of Technology (KIT), Forschungszentrum Jülich (FZJ), Helmholtz-Zentrum hereon GmbH (Hereon), Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the German Aerospace Center (DLR), representing the six research fields of the Helmholtz Association.
The Incubator
In 2016, Helmholtz initiated the Helmholtz Information & Data Science Incubator in order to network and strengthen the Association’s expertise and enormous stores of data. This long-term, bottom-up process spanning the entirety of Helmholtz pools the Association’s diverse expertise in the pioneering field of Information & Data Science. Today, the Incubator consits of five core platforms - HIDA, HIFIS, Helmholtz Imaging, HMC and Helmholtz AI.
International labs
The 'Helmholtz International Labs' funding program of the Helmholtz Association was created within the framework of the Initiative and Networking Fund to strengthen strategic international cooperation with excellent research institutions from around the world. Helmholtz AI scientists are involved in two of these amazing cooperations:
Helmholtz International BigBrain Analytics and Learning Laboratory
HIBALL is the funding umbrella for the BigBrain Project. It aims to transform the successful collaboration between McGill (MNI, BIC) and Forschungszentrum Jülich (INM, JSC) to the next level by reinforcing utilization and co-development of the latest AI and high-performance computing (HPC) technologies for building highly detailed 3D brain models. It establishes a close collaboration with CIFAR and MILA in Canada, and Helmholtz AI in Germany.
CausalCellDynamics Lab
The CCD adapapts modern causal learning models to high-dimensional perturbation data as occurring in single-cell genomics and combine this with methods from computational biology to model gene regulatory interactions.
The lab focuses on developing robust causal models in high dimensions, learning deep representations of cell ensembles in response to perturbations, and adding biological priors and interpret resulting trained models using experimental data such as epigenomics.
