Success for the Helmholtz Association & its Data Science platforms: six projects secure BMBF funding
The Federal Ministry of Education and Research (BMBF) has announced a €30 million investment in 15 cutting-edge projects in AI & ML under its initiative "Flexible, Resilient, and Efficient Machine Learning Models". This funding is part of the German government's AI strategy, aimed at addressing key challenges in artificial intelligence and machine learning, such as robustness, efficiency, and sustainability. These projects span diverse applications, from climate modelling to biomedical research, and emphasize interdisciplinary and European collaboration. Six of these projects are led or co-led by researchers from the Helmholtz Association, reaffirming its leading role in driving AI innovation for science.
The success of these projects also reflects the positive effect of the Helmholtz Association’s Information and Data Science Platforms, which foster collaboration, provide high-level research, offer various information & data science services, as well as AI consulting, organise events, and promote essential exchange. These significant contributions demonstrate the platforms’ crucial role in advancing innovation across the Helmholtz Association through novel AI and data science solutions.
ALL HELMHOLTZ-AFFILIATED AWARDED PROJECTS
- CausalNet – Nadja Klein (KIT, Helmholtz AI Associate), Stefan Bauer (Helmholtz AI Lab PI), Niki Kilbertus (Helmholtz AI Lab PI);
- COMFORT – Martin Burger (DESY, Helmholtz Imaging);
- FAIME – Stefan Kesselheim (FZJ, Helmholtz AI Consultant Head, HFMI Synergy Unit);
- KI-HopE-De – Ralf Loritz (KIT, Helmholtz AI Associate).
- OPENHAFM – Jenia Jitsev (FZJ).
- RAINA – Martin Schultz (FZJ, HFMI).
PROJECT HIGHLIGHTS BY OUR RESEARCHERS
Find full project descriptions here.(German only)
CausalNet: A framework for integrating causality for flexible, robust and efficient machine-learning models
- Lead Helmholtz PIs: Nadja Klein (KIT, Helmholtz AI Associate), Stefan Bauer (Helmholtz AI Lab PI), Niki Kilbertus (Helmholtz AI Lab PI)
- Partners: Karlsruhe Institute of Technology (KIT), Helmholtz Zentrum München (HMGU), Ludwig-Maximilians-Universität München.
- Timeframe: October 2024 – September 2027.
- Objective: Develop a causal ML framework to improve model robustness and performance by integrating causal reasoning into ML architectures. The project will create open-source causal tools, benchmarks, and datasets applicable across healthcare, bioinformatics, and public services.
Read more about this project in the press release “New Collaborative Project: Artificial Intelligence That Recognizes Causal Relationships” by Helmholtz Munich.
FAIME: Flexible, efficient AI-driven molecular simulation
- Lead Helmholtz PI: Stefan Kesselheim (FZJ, Helmholtz AI Consultant Head, HFMI Synergy Unit)
- Partners: Forschungszentrum Jülich, Freie Universität Berlin.
- Timeframe: October 2024 – September 2027.
- Objective: Accelerate molecular simulations to study protein dynamics critical for understanding diseases like Alzheimer’s. The project will use graph neural networks and physical theories to create open-source tools applicable to medicine and materials science.
Read more about this project in the press release “Electricity Consumption and Reliability – AI in Application” by Forschungszentrum Jülich (German only).
KI-HopE-De: AI-based flood forecasting for small catchment areas in Germany
- Lead Helmholtz PI: Ralf Loritz (KIT, Helmholtz AI Associate).
- Partners: German Weather Service, State Offices for Nature and Environment (North Rhine-Westphalia and Rhineland-Palatinate)
- Timeframe: December 2024 – November 2027.
- Objective: Enhance flood forecasting in small river catchments using AI, improving accuracy and lead time. The project will develop a prototype platform for nationwide adoption to bolster public safety.
PROJECT HIGHLIGHTS BY RESEARCHERS @HELMHOLTZ IMAGING
COMFORT: Compression methods for robustness and transfer
- Lead Helmholtz PI: Martin Burger (DESY, Helmholtz Imaging).
- Partners: Universität Würzburg, DESY, Technische Universität München, Universität Hamburg und Friedrich-Alexander-Universität Erlangen-Nürnberg
- Timeframe: October 2024 – September 2027.
- Objective: Design novel compression techniques to optimize the performance, robustness, and efficiency of ML models, with applications in imaging and industrial processes.
Read more about the “COMFORT: Compression methods for robustness and transfer“ project in the "Driving Sustainable AI: BMBF Funding for the Helmholtz Association & its Data Science Platforms for Machine Learning Innovation" release by Helmholtz Imaging.
OTHER HELMHOLTZ-AFFILIATED PROJECTS
Find full project descriptions here.(German only)
OPENHAFM: Evaluating and improving open foundation models through systematic human alignment benchmarking and dataset curation
- Lead Helmholtz PI: Jenia Jitsev (FZJ).
- Partners: Max Planck Institute for Intelligent Systems, University of Tübingen, Forschungszentrum Jülich.
- Timeframe: October 2024 – September 2027.
- Objective: Improve foundation models, like ChatGPT, by aligning them with human judgment and logical reasoning. The project will curate benchmarks and datasets to ensure reliable performance in critical applications.
RAINA: A statistically robust AI foundation model of the atmosphere for better short-term predictions of extreme events
- Lead Helmholtz PI: Martin Schultz (FZJ, HFMI).
- Partners: European Centre for Medium-Term Weather Forecasts, University of Bonn, German Weather Service.
- Timeframe: November 2024 – October 2027.
- Objective: Develop the first AI foundation model of the atmosphere to enhance short-term extreme weather forecasts with unprecedented resolution and accuracy, addressing societal and climate challenges.