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.
Join us in unlocking the limitless potential of Helmholtz Imaging!
Find out more about Helmholtz Imaging Project call in this summary.
Helmholtz Imaging Projects
Image: Johannes Seiffarth, FZ Jülich
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.
BRLEMM
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.
Image: PlastoView
PlastoView
Microplastic Detection with the PlastiScope
Water quality is essential for ecosystems and human health, yet it's increasingly threatened by microplastics. This project develops image-based methods for detecting both plankton and microplastics using a new low-cost, mobile system.Helmholtz Foundation Model Initiative (HFMI) Projects
Image: NicoElNino on Shutterstock
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.
Image: NicoElNino on Shutterstock
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.
Image: NicoElNino on Shutterstock
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.UNLOCK – Benchmarking Projects
RenewBench – A Global Benchmark for Renewable Energy Generation
Renewable energy’s variability makes grid management complex. RenewBench aims to provide standardized, high-quality data to advance trustworthy AI models and accelerate the transition to sustainable energy.
Image: Open white spruce forest with glacier in background in the Chugach Mountains, Alaska, US ©Stefan Kruse, AWI
ForestUNLOCK: A multi-modal Multiscale Benchmark Dataset for AI-Driven Boreal Forest Monitoring and Carbon Accounting
Building the first consistent multi-modal single tree benchmark for forest structure and carbon stock assessments of the northern boreal forest
Image: Georg Kislinger, Martina Schifferer, Christian Haass & Maryam
Khojasteh-Farat, DZNE (BSIC 2021 contribution)
NeuroHarmonize – A Benchmark Decentralized Data Harmonization Workflow for AI-Driven Alzheimer’s Disease Management
The benchmark addresses the lack of harmonized, reproducible, and privacy-preserving multimodal datasets for Alzheimer’s disease (AD). NeuroHarmonize creates a FAIR-compliant, decentralized benchmarking framework to accelerate reliable, transparent, and collaborative AI for AD diagnosis, prognosis, and long-term monitoring.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.
SFB Transregio 154 – C06: Transport metrics for analysis and optimization of network problems
SFB TRR 154 is a project of the German Research Foundation (DFG) and combines integer-continuous methods, model adaptation, and numerical simulation, to analyze and optimize gas markets, infrastructure, and control of networks. The third funding period specifically focuses on the transition from natural gas to hydrogen.