One focus of the initiative is the call for proposals and funding of groups under the DFG’s Emmy Noether Programme. The aim is for these groups to attract the next generation of highly qualified researchers with a focus on AI methods by opening up career opportunities that offer a high degree of independence at an early stage. The initiative enables up to 45 Emmy Noether Groups to be established across four rounds of calls.

• 2026/2027(Anchor Link)
• 2025/2026(Anchor Link)
• 2020/2021(Anchor Link)
• 2019/2020(Anchor Link)

2026/2027:

Call 4: Information for Researchers No. 24 | 1 April 2026(interner Link)

The list of funded projects, arranged in alphabetical order according to name of grantee, will be announced in respective time.

2025/2026:

Call 3: Information for Researchers No. 27 | 25 March 2025(interner Link)

Funded projects in alphabetical order according to name of grantee:

• "ACTIVUS: Representations and Foundation Models for Actionable Visual Understanding"(externer Link) - Nikita Araslanov
• "From Mediocre to Masterful Generalists: The Power of Context in RL"(externer Link) - André Biedenkapp
• "Causality and representation learning for interventional foundation models"(externer Link) - Simon Buchholz
• "AI unlocks the Higgs boson’s potential"(externer Link) - Nicole Hartman
• "Flow-based Generative Models and Optimal Transport for Sampling and Inverse Problems"(externer Link) - Johannes Hertrich
• "Neuro-Symbolic Methods in Sequential Decision Making"(externer Link) - Daniel Höller
• "Enhancing Network Efficiency through Combined Weight Space Learning and Architecture Search"(externer Link) - Jovita Lukasik
• "The sense of control in natural and artificial intelligence"(externer Link) - Alireza Modirshanechi
• "Making AI Understand Structure: Improving Vision (Language) Models through Graphs, Topology, and Human-Aligned Design"(externer Link) - Johannes C. Paetzold
• "AI Methods for Measuring and Mitigating Political Bias in AI Systems"(externer Link) - Paul Röttger
• "Earth Embeddings: Implicit Neural Representations across Geospatial Data Modalities"(externer Link) - Marc Rußwurm
• "RMT4DL: Random Matrix Theory for Deep Learning"(externer Link) - Mariia Seleznova
• "Verification for Learning and Learning for Verification"(externer Link) - Chelsea Rose Sidrane
• "Scalable Automated Reasoning"(externer Link) - Dominik Schreiber
• "Multimodal representation learning for generalizable patient embeddings"(externer Link) - Sophia Wagner

2020/2021:

Call 2: Information for Researchers No. 83 | 17 November 2020(interner Link)

Funded projects in alphabetical order according to name of grantee:

• "Harmonic Artificial Intelligence based on Linear Operators"(externer Link) - Dr. Felix Dietrich
• "Robust Computer Vision through Neural Analysis-by-Synthesis with 3D-aware Compositional Network Architectures"(externer Link) - Dr. Adam Kortylewski
• "Graph Embeddings: Theory meets Practice"(externer Link) - Dr. Christopher Morris
• "Trustworthy Reinforcement Learning for Multi-Agent Systems: Foundations of Robust and Accountable Decision Making"(externer Link) - Dr. Goran Radanovic
• "Efficient Learning for Transferable Robot Autonomy in Human-Centered Environments"(externer Link) - Prof. Dr. Abhinav Valada
• "Foundations of Lifelong Reinforcement Learning"(externer Link) - Prof. Dr. Claire Vernade

2019/2020:

Call 1: Information for Researchers No. 74 | 31 October 2019(interner Link)

Funded projects in alphabetical order according to name of grantee:

• "Eidetic Representations of Natural Language"(externer Link) - Prof. Dr.-Ing. Alan Akbik
• "Resource-Efficient Bayesian Machine Learning"(externer Link) - Prof. Dr. Robert Bamler
• "Strong response consistency for more robust, disentangled and generalizable machine vision"(externer Link) - Dr. Wieland Brendel
• "Robot Learning of Mobile Manipulation for Intelligent Assistance"(externer Link) - Prof. Georgia Chalvatzaki Ph.D.
• "Motion Coordination for Heterogeneous Aerial Swarms in Congested Environments"(externer Link) - Wolfgang Hönig Ph.D.
• "Intuitive Robot Intelligence: Efficiently Learning and Improving of Explainable Skills and Behaviors for Intuitive Human-Robot Interaction."(externer Link) - Dr.-Ing. Rudolf Lioutikov
• "Decision-making under uncertainty: including prior knowledge from physics"(externer Link) - Dr. Michael Mühlebach
• "Approximation Algorithms for Geometric Data Analysis and Their Practicability"(externer Link) - Prof. Dr. Melanie Schmidt
• "Stability and Solvability in Deep Learning"(externer Link) - Prof. Dr. Felix Voigtlaender