Published on 20.01.2023
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.
Published on 20.01.2023
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.
Published on 20.01.2023
How can the manufacturing processes of materials be mapped at the smallest level? How do you train an artificial intelligence to analyze these processes automatically? That’s the focus of the Avanti project, which aims to improve X-ray tomoscopy – the imaging and quantification of three-dimensional images of very fast-moving processes.
Published on 20.01.2023
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.
Published on 20.01.2023
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.
Published on 20.01.2023
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.
Published on 20.01.2023
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.
Published on 20.01.2023
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.
Published on 20.01.2023
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.
Published on 19.01.2023
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.
Published on 15.12.2022
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.
Published on 15.12.2022
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.
Published on 12.12.2022
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 […]
Published on 08.12.2022
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 […]