Our Projects
Helmholtz Imaging Projects aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science.
Funds for Helmholtz Imaging Projects are annually granted to cross-disciplinary research teams for collaborative mid-term projects.
Ideally, Helmholtz Imaging Projects are co-created with users and non-academic stakeholders to ensure the quick adoption of results.
Funding for the first Helmholtz Imaging projects started in December 2020. Many teams have since begun work on major challenges and pressing issues facing society to develop sustainable solutions for tomorrow and beyond.
Discover these outstanding and fascinating research projects with us or become a part of Helmholtz Imaging Projects and apply for your own project.
Projects - Ongoing
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.
Solar Image-based Modelling
The aim of the project is to develop an algorithm by which computers can automatically predict the space weather. This will make use of datasets of solar images that have been captured from space. The method could replace computationally demanding physics-based models and deliver space weather forecasts long before the effects of solar events are felt.
The Hidden Image of Thawing Permafrost
The project aims to develop a method for determining just how extensively thaw processes have already progressed in permafrost regions. The machine learning approach to be developed will be used to analyse radar images from aircraft in order to learn more about the properties of the subsurface permafrost.
Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service
In order to detect impending landslides before they occur and to enable reliable emergency mapping after a landslide, the researchers are combining optical data with radar data from satellites. Using machine learning methods, computers will be trained to recognise the tiniest of changes in things like sloping landscape surfaces.
Tackling the segmentation and tracking challenges of growing colonies and microbialdiversity
An artificial intelligence will observe the growth of bacteria: from microscope images of bacterial cultures taken at regular intervals, it will precisely track the development and division of individual cells – even when multiple bacterial species are cultivated together.
Geophysical Joint Inversion for Accurate Brain Myelin Mapping
The aim of this project is to develop a method for clinically diagnosing neurodegenerative diseases. The content of myelin in the brain – a substance that becomes degraded in diseases – will be quantified using methods from geophysics in order to facilitate early detection and treatment.
Breaking resolution limit of electron microscopy for magnetic materials
A new method will make it possible to take images of the magnetic properties of materials under the electron microscope and to correlate these properties with their atomic structure. In order to achieve high resolution, a special algorithm must be developed to compute the magnetic properties from the microscope data.
Artificial Intelligence for 3D multimodal point cloud classification
The aim is to develop an artificial intelligence that can achieve the fusion of two-dimensional data with three-dimensional information. Based on this, the software would simultaneously be able to recognise image characteristics as well as the spatial relationships between different objects.
Neuroimaging Biomarkers for Restless Leg Syndrome
The aim is to develop a software solution that can analyse enormous amounts of data on tens of thousands of subjects from large-scale health studies. Using restless leg syndrome as an example, genomic data will be combined with neuroimaging data in order to identify new biomarkers with the help of machine learning methods.
Ultra Content Screening for Clinical Diagnostics and Deep Phenotyping
A method will be developed in which selected biomarkers in tumour and bone marrow cells from cancer patients will be examined and analysed automatically. The novel technology is based on ultra content screening technology, which allows detailed insights at the single cell level.
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.