Projects
With our Helmholtz Imaging Projects, Helmholtz Foundation Model Initiative (HFMI) 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 team has secured external funding for third-party projects contributing their knowledge and expertise on cutting-edge imaging methodology.
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Find out more about Helmholtz Imaging Project call in this summary.
Helmholtz Imaging Projects
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 […]
Fast-EMI
Deep-learning assisted fast in situ 4D electron microscope imaging
A novel imaging approach combining electron microscopy and deep learning has been established. This method enables adaptive tracking of atomic defects, accelerating material development for the energy transition.
EMSIG
Event-driven Microscopy for Smart Microfluidic Single-cell Analysis
Microfluidic live-cell imaging (MLCI) unlocks spatio-temporal insights into population heterogeneity emerging from a single cell. EMSIG brings smart live-event detection capabilities to MLCI to facilitate the adaptive optimization of biological event resolution and autonomously counteracting deteriorating image qualities.Helmholtz Foundation Model Initiative (HFMI) Projects
Synergy Unit
The Synergy Unit amplifies the Helmholtz Foundation Model Initiative's impact by developing AI principles for diverse fields. Collaborating with HFMI projects, it focuses on knowledge sharing, community building, and representation to ensure the initiative's lasting influence.
The Human Radiome Project (THRP)
The Human Radiome Project (THRP) aims to drive a paradigm shift in medical research, providing novel insights into human health and disease through the power of AI. By integrating diverse radiological data, it seeks to enable groundbreaking advancements in personalized medicine, enhancing diagnostic accuracy and improving patient care.
AqQua
AqQua aims to build the first foundational pelagic imaging model using billions of aquatic images worldwide. These images, spanning species from plankton, will help an AI classify species, extract traits, and estimate carbon content, offering key insights into biodiversity, ecosystem health, and the biological carbon pump's role in climate regulation.Third-Party Projects
Foundations of Supervised Deep Learning for Inverse Problems
Recently, deep learning methods have excelled at various data processing tasks including the solution of ill-posed inverse problems. The goal of this project is to contribute to the theoretical foundation for truly understanding deep networks as regularization techniques which can reestablish a continuous dependence of the solution on the data.
FONDA: Dependability, Adaptability and Uncertainty Quantification for Data Analysis Workflows in Large-Scale Biomedical Image Analysis
The project aims to enhance infrastructures for machine learning (ML)-intensive DAWs in advanced biomedical imaging applications.