Projects
With our Helmholtz Imaging Projects and third-party funded projects, we aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science.
Helmholtz Imaging offers a funding line of Helmholtz Imaging Projects, striving to seed collaborations between centers and across research fields. They are a strong incentive to enable interdisciplinary collaboration across the Helmholtz Association and an incubator and accelerator of the Helmholtz Imaging network.
In addition to our Helmholtz Imaging Projects, the Helmholtz Imaging te am has secured external funding for third-party projects contributing their knowledge and expertise on cutting-edge imaging methodology.
Join us in unlocking the limitless potential of Helmholtz Imaging!
The next call for Helmholtz Imaging Projects will open in spring 2025. Stay tuned!
Helmholtz Imaging Projects
AIOrganoid
Artificial Intelligence Assisted-Imaging for Creating High-yield, High-fidelity Human Lung Organoid
AIOrganoid will apply cutting-edge imaging techniques and develop novel AI-based solutions to facilitate human lung organoid formation with high yield and fidelity, bridging the gap between cell biology and computational imaging.
AsoftXm
Advanced Soft-X-Ray Microscopy Solutions
The project aims to develop a method that will speed up the analysis of diffraction patterns that arise in UV and soft X-ray light microscopy, so that the structure of the studied sample can be calculated more efficiently. The method could make the three-dimensional study of nanomaterials considerably easier. There are times when researchers need […]
SyNaToSe
Leveraging Cross-Domain Synergies for Efficient Machine Learning of Nanoscale Tomogram Segmentation
The aim is to develop an adaptable algorithm that can be used to perform different tasks in data and image analysis without needing to be trained with new, laboriously annotated images for each separate task.Third-Party Projects
Deep Learning based Regularization for Inverse Problems
This project aims to investigate the construction of regularization methods for ill-posed inverse problems based on deep learning and their theoretical foundations. Specific objectives include the development of robust and interpretable results, requiring the initial development of new concepts of robustness and interpretability in this context.
Bayesian Computations for Large-scale (Nonlinear) Inverse Problems in Imaging
During research stays with the collaborating group at Caltech, we aim to investigate various aspects of statistical inverse problems. This includes inquiries into particle- and PDE-based sampling methods, as well as robust regularization using neural networks.