Helmholtz Imaging 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. The new call will be published in spring 2024. Stay tuned!
Helmholtz Imaging Projects – Project overview
NImRLS
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.
SATOMI
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.
SIM
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 […]
SmartPhase
Fast, intelligent 3-D X-ray images for material examination
In order to be able to improve materials, it helps to take a look at their microstructures. This is because valuable information about their properties and behaviour can be found there – for example information about when which ageing processes begin. The aim of this project is to automate and accelerate access to this information with the help of a smart imaging technique.
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.
TerraByte-DNN2Sim
On the trail of the mystery of the laws of calving
Researchers still face a mystery when it comes to the laws by which glaciers calve. This project aims to use satellite imagery, artificial intelligence, mathematical optimisation and a new data processing pipeline to track the movements of glacier fronts in Antarctica to get closer to solving the mystery.
UCS
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.
UTILE
Autonomous image analysis to accelerate energy materials discovery and integration
Research into green materials for clean energy generation is moving at full speed – yet still requires a long time to complete. This project is working on an open source image processing application that uses artificial intelligence to drive the analysis and management of image data from experiments across the energy materials community.
WeMonitor
Satellite-based Earth observation to detect natural hazards
Satellite imagery makes it possible to detect spatio-temporal anomalies on the Earth’s surface, including natural hazards such as landslides, deforestation, or the emergence of large waste dump sites. This project aims to use artificial intelligence to detect these changes at an early stage and to be able to monitor their progress.