Welcome to ...
the Konrad Zuse Schools of Excellence in Artificial Intelligence
In a nutshell
graduate schools
sites
network partners from science and industry
fellows
PhD and master's students
What are the Zuse Schools?
The DAAD provides funding for the Konrad Zuse Schools of Excellence in Artificial Intelligence – three graduate schools that train international and German AI talents at master’s and PhD level in Germany. The programme is financed by the Federal Ministry of Research, Technology and Space (BMFTR), was created on the basis of the country’s nationwide AI strategy and feeds into the German government’s High-Tech Agenda.
Advantages for Germany as a centre for AI
⦁ We foster transfer between science and industry in key areas such as the medicine of the future and start-ups.
⦁ We facilitate access to AI capacities - from algorithms and tools to chips - for research, industry and society.
⦁ We strengthen Germany as a global AI competitor.
Benefits for scholarship holders
⦁ tuition-free master’s degree course in Germany, scholarships for master’s students
⦁ paid research position with a fixed salary
⦁ mentoring by experts from science and industry
⦁ support, networking and further training measures
⦁ connection to a strong AI network
Our talents
Let us introduce you to young researchers who are advancing artificial intelligence in Germany
AI talents for Germany
When AI learns to see
What makes computer vision possible
Mohamed Afham is researching how machines can interpret and understand our world visually. His focus is on computer vision and what is known as procedure planning. He uses the example of a recipe to explain: when we have the ingredients and utensils in front of us, our experience and ability to think logically immediately tell us what step we have to take next. And the idea is for robots to learn in exactly the same way how to translate visual impressions into logical sequences of actions.
Research that makes an impact
The range of possible applications is huge: everything from the early detection of wear in machines or the analysis of medical images to identify tumours to the use of visual real-time systems to support surgeons.
Computer vision is addressed in the research field “Foundations of Machine Learning”, which is one of the four main focal areas of research and teaching at ELIZA, the Konrad Zuse School of Excellence in Learning and Intelligent Systems.
One of the things that sets ELIZA and the Zuse Schools apart is the close supervision that is provided. Master’s students like Mohamed Afham are given insights at an early stage into PhD positions and can transition seamlessly from the master’s to a PhD at our school.
Professor Stefan Roth, Director of the Zuse School ELIZA
Useful information about ELIZA
What is the focus at ELIZA?
Research and training at ELIZA focus on four main areas:
⦁ the foundations of machine learning (including disciplines such as computer vision, natural language processing and robot learning)
⦁ machine learning systems
⦁ applications in autonomous systems
⦁ and transdisciplinary applications for machine learning in other scientific disciplines, from the life sciences to physics.
At which universities can students study and conduct research if they enrol at ELIZA?
ELIZA is coordinated by TU Darmstadt but has additional sites in Berlin, Freiburg, Heidelberg, Munich, Saarbrücken and Tübingen.
How are ELIZA and ELLIS linked?
ELIZA is based on ELLIS, the European Laboratory for Learning and Intelligent Systems. It combines seven German ELLIS units and is part of Europe’s leading AI network with over 300 members. They include the German Cancer Research Center and the Max Planck Institute for Software Systems.
What examples are there of particular achievements by ELIZA fellows and students?
ELIZA fellows and students have published internationally recognised papers, been awarded over 30 ERC grants and won renowned prizes such as the Alfried Krupp Prize, the Royal Society Milner Award and the German Cancer Award. In addition, students regularly present their research at leading conferences such as NeurIPS, ICML and many others.
Research at top-class level
Building trust in AI
I joined relAI as a master’s student and was part of the first cohort – twelve young AI enthusiasts from all over the world. I’m now doing my PhD at relAI, I’m a student representative and have witnessed over the past few years how the graduate school has grown and grown.
Nil Ayday
PhD student at relAI
Voices from the network
The Konrad Zuse Schools of Excellence in Artificial Intelligence are a great advertisement for Germany – attracting international AI talents to the country and motivating them to stay. They help train experts who are urgently needed to develop reliable AI systems end-to-end.
BMW
Collaborating with a researcher at the Zuse School relAI was an extremely enriching experience that noticeably benefited my own research. We received valuable insights during his stay, resulting in a joint prize-winning publication. I am delighted about this partnership and the many opportunities it opens up for international scientific exchange.
Juan Carlos Perdomo, assistant professor
of computer science and data science at New York University (NYU)
Network of excellence
From Germany to the world – a global AI alliance
Around 70 partner institutions from science and industry are part of the Zuse Schools network. Through international collaborations, fellows and students, they play an important role in implementing the German government’s High-Tech Agenda.
Network map of the Zuse Schools
Where science meets practice
Nil Ayday with doctoral candidates from other departments at relAI
Professor Debarghya Ghoshdastidar is researching the theory of machine learning, artificial intelligence and network science.
Interdisciplinary exchange is an important element at the Zuse Schools.
Nil Ayday with her doctoral supervisor Professor Debarghya Ghoshdastidar
Besides basic research, work is also done on practical applications.
Close supervision helps Nil Ayday resolve current research questions.
PhD students with Professor Debarghya Ghoshdastidar
Researchers from relAI using the supercomputer resources at the Leibniz Supercomputing Centre in Garching.
Useful information about relAI
What is the focus at relAI?
relAI focuses on the challenge of making artificial intelligence reliable. Research and teaching at relAI combine the mathematical and algorithmic foundations of reliable AI with expert knowledge in four core application domains:
⦁ medicine and healthcare,
⦁ robotics and interacting systems
⦁ and algorithmic decision-making.
Currently, work is underway to launch a new field of application designed to cover the area of reliable AI in learning and instruction.
At which universities can students study and conduct research if they enrol at relAI?
relAI is a joint initiative of two universities of excellence, the Technical University of Munich (TUM) and Ludwig-Maximilians-Universität München (LMU Munich).
What examples are there of particular achievements by relAI fellows and students?
relAI fellows and students publish their research findings in internationally renowned conference proceedings such as NeurIPS, ICLR, ICML and CVPR, as well as in journals such as those published by Springer Nature. Their successful achievements include an IJAR Young Researcher Award and an ICML Outstanding Paper Award. Students have also scooped prizes for their research at leading conferences such as NeurIPS, ICLR and ICML, receiving three best paper awards and a runner-up award.
What science needs
AI that is as energy-efficient as our brain
I was searching for PhD positions worldwide and, at the Zuse School SECAI here in Germany, was pleasantly surprised to find an interdisciplinary research topic that I'm really enthusiastic about.
Tim Langer
PhD student at SECAI
Start-up factory
The spin-off SpiNNcloud is one of many examples of how scientific knowledge is directly translated into industrial applications at SECAI and the Zuse Schools. Their contact with network partners and the start-up information events encourage scholarship holders and fellows to start their own businesses. “On the academic side I’m researching the SpiNNaker2 chip, while in our start-up we’re already working on marketing it and enabling its commercial use,” explains Tim Langer.
The SpiNNaker2 chip will allow highly energy-efficient processing of imaging and video data – one possible application could be in the medical domain. For example, it could analyse video feeds from the operating theatre in real time and support assisted systems that work out which surgical instrument will be needed in the next step.
Funding for more innovation
One of the core objectives of the German High-Tech Agenda is to make funding available for AI spin-offs from science and academia and to support innovative start-ups. This is crucial to ensure that Germany remains competitive as a centre for AI.
The Zuse Schools of Excellence in Artificial Intelligence support spin-offs from their departments and help students and PhD candidates to launch their own start-ups.
The SpiNNaker2 chip offers great potential for real-time processing of intraoperative imaging and video data during computer- and robot-assisted surgery. Tim Langer’s work on event-based data processing allows time-critical changes in surgical settings to be captured with a significantly reduced processing and communication load. In the long term, this will allow adaptive, energy-efficient systems based on SpiNNaker2 to be developed to support surgical decision-making.
Professor Stefanie Speidel, Deputy Director of the Zuse School SECAI
Useful information about SECAI
What is the focus at SECAI?
The Zuse School of Embedded Composite Artificial Intelligence (SECAI) focuses on two core goals:
⦁ the development of novel AI methods that combine the advantages of previously separate approaches and
⦁ the integration of AI algorithms into tailormade microelectronics and intelligent devices.
Among the many application areas of AI, SECAI places special emphasis on digital medicine. This field offers great opportunities: for example through the development of intelligent medical devices – from surgical robotic assistants to intelligent pacemakers – and through advances in medical informatics and bioinformatics, such as in the development of personalised drugs or improved cancer diagnostics.
At which universities can students study and conduct research if they enrol at SECAI?
SECAI is a joint project of TU Dresden and Leipzig University.
What examples are there of particular achievements by SECAI fellows and students?
SECAI PhD students have published over 200 papers in scientific journals and won renowned prizes such as best paper awards at a wide range of conferences, including the International Conference on Principles and Practice of Multi-Agent Systems, the ACM International Conference on Hybrid Systems: Computation and Control and the International Conference on Principles of Knowledge Representation and Reasoning. They also were among the winners of the Algonauts Project 2025 Challenge.
Zuse School outcomes
internships in Germany
start-ups in Germany
publications
internationally acclaimed publications
Curious?
Find out more about the Konrad Zuse Schools of Excellence in Artificial Intelligence and their network:
Imprint
Publisher
Deutscher Akademischer Austauschdienst e.V. (DAAD) Kennedyallee 50 D-53175 Bonn (Germany)
Tel.: +49 228 882-0 Fax: +49 228 882-444
E-Mail: webmaster@daad.de Internet: www.daad.de/en
Authorised Representative of the Executive Committee: Professor Joybrato Mukherjee District Court of Bonn Register of associations, number VR 2107 Sales tax number: DE122276332
Person responsible according to § 55 Abs. 2 RStV: Dr Kai Sicks, Kennedyallee 50, 53175 Bonn
The DAAD is an association of German universities and their student bodies. It is institutionally funded by the German Federal Foreign Office.
Project coordination
Birgit Siebe-Herbig (responsible) Julia Kracht Araújo Christian Braselmann Saskia Illing
Text, video and design
Fazit Communication GmbH Frankfurt am Main
Photo credits: DAAD/Dean Gold, DAAD/Fazit Communication, DAAD/Jessica Krauß, DAAD/Marius Kohl, DAAD/Nathan Dreessen, TU Darmstadt