Interdisciplinary graduate school

The Zuse School relAI is a project run jointly by the Technical University of Munich (TUM) and Ludwig-Maximilians-Universität München (LMU). The programme is interdisciplinary and cross-sectoral, encompassing both research and practical modules. The curriculums of the master’s and PhD programmes comprise four components: lectures and seminars (optional for doctoral candidates), industry internships and stays abroad, courses in soft skills and science communication, and a final thesis.

Cross-site supervision by academic fellows

The cross-site research and teaching on offer is based on the concept of supervision by academic fellows and external industry partners. At master’s level this takes the form of one-to-one supervision by a relAI fellow, and students can write their master’s thesis in cooperation with an industry partner. Doctoral candidates are supervised by a team of relAI fellows and external partners who provide advice and support with technical aspects of the thesis and with personal development. Furthermore, doctoral candidates can themselves mentor master’s students.
 

Outstanding mentoring

The 33 academic fellows currently at relAI form the core element of the school. They provide structured mentoring even during the master’s programme. You will then work with one of the fellows and a doctoral candidate. Would you like to know which master’s students and doctoral candidates are currently being funded by relAI? An overview can be found here.

 

 

Applications are submitted directly to the Zuse School.

Master’s

German and international students who have graduated with an excellent bachelor’s degree (or equivalent) in computer science, mathematics, engineering, the natural sciences or other disciplines relevant to data science/machine learning/AI and have been accepted onto a master’s degree course in computer science, mathematics or related subjects at the Technical University of Munich (TUM) or Ludwig-Maximilians-Universität München (LMU) can apply for a master’s scholarship. Up-to-date information about applying for the next cohort can be found here. You will also receive details about the next rounds of application for a master’s scholarship by e-mail if you sign up to this mailing list.

PhD

Graduates with an excellent master’s degree (or equivalent) in computer science, mathematics, engineering, the natural sciences or other disciplines related to data science/machine learning/AI can apply for PhD funding. Up-to-date information about applying for the next cohort can be found here. You will also receive details about the next rounds of application for the PhD programme by e-mail if you sign up to this mailing list.

Associated master’s or PhD

In addition, it is possible to become a relAI associated PhD candidate. If for example your research field is relevant to relAI, one of your supervisors is already a relAI fellow and has third-party funding, you can become an associated PhD candidate. Associated PhD candidates follow the same curriculum and receive travel grants.
 

The Konrad Zuse School of Excellence in Reliable AI focuses on four main areas:

Safety 

The aim is to discuss how AI tools can be designed so as not to cause any harm or danger.

Security

Security is all about how to make AI systems as resilient as possible so that they can withstand attacks and information leaks.

Privacy

Privacy explores methods of ensuring the protection and confidentiality of data.

Responsibility

In the area of responsibility, approaches are developed to implement social norms and ethical principles in AI systems.

All of these topics are addressed during study of the mathematical and algorithmic foundations, as well as in the three application domains of medicine and healthcare, robotics and interacting systems, and algorithmic decision-making.
 

The Konrad Zuse School relAI network comprises a large number of partners in academic and business contexts. 

Academic partners

Academic partners currently include: Data Science @ Uni Vienna, Alan Turing Institute, Center for Data Science (CDS) at New York University, Center for Statistics and Machine Learning (CSML) at Princeton University, Center of Data Science and AI Research (CeDAR) at the University of California, Davis, Duke Rhodes Information Initiative (RII) at Duke University, Mathematical Institute for Data Science (MINDS) at John Hopkins University, NSF AI Institute for Advance in Optimization (AI4OPT) at the Georgia Institute of Technology, Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, Stanford Data Science at Stanford University, Switzerland Center for Intelligent Systems (CIS) at the École Polytechnique Fédérale de Lausanne, Center for Responsible AI Research (coordinated by the Munich School of Philosophy), Helmholtz Munich, Center for AI Research (CAIRE) at Hong Kong University of Science and Technology.

Industry partners

Industry partners currently include: Allianz, BMW, Bosch, Celonis, Denso, Frauenhofer AISEC, Frauenhofer IIS, Frauenhofer IKS, Google, Imfusion, Infineon, Linde, MunichRe, Siemens, Siemens Healthineers, UnternehmerTUM, VW.

 

TUM is involved in two of the three Zuse Schools. Professor Stephan Günnemann, the school’s spokesperson and a professor of data analytics and machine learning at TUM, explains how important cooperation on the subject of AI reliability is, both at the local level and with international partners.

Professor Günnemann, what sets relAI apart as an excellent graduate school for budding AI experts?

In focusing on the reliability of AI technology, the project partners TUM and LMU are addressing a highly relevant topic that is not only of technological interest. It also has a strong social impact, as people will not wish to use AI methods in society unless they are secure and reliable. This focus is decisive, and what sets us apart.

How do you research the reliability of AI?

On the one hand, we combine basic and methodological research with direct application in relevant domains, such as robotics, driverless vehicles, medicine and decision-making. We work with a broad spectrum of researchers – mathematicians and computer scientists, but also experts from the different areas of application. We also attach importance to this interdisciplinary collaboration during training, our doctoral students being supervised by experts from both method-based and application-oriented research. In the medium term, we are also planning to cooperate closely with industrial partners so that the methods developed at our school can be trialled in companies.

You yourself have worked in the research department of a company, so you also know the other side. Does a lot need to be done in Germany to bring science and business closer together?

AI is a good area in which to work closely together, as there is always a concrete objective. Industry has a good knowledge of the real-life problems, which makes cooperation particularly productive. At relAI, it will also be a question of bringing master’s students into contact with firms at an early stage. After all, the goal of the Zuse Schools is not only to convey practical experience in close exchange with business. Candidates are also to be given the chance to discover everything that can be done in the area of AI – many students are not aware of all the potential applications.

TUM has been receiving the DAAD funding since July 2022. What are the next steps?

We have already staged our first Fellow Assembly, which brought all the founding members together and also saw the boards formally elected. Currently we are busy fine-tuning the curriculum and consolidating the teaching that will be offered during the upcoming semester. One major advantage is that the graduate school is embedded in TUM’s Munich Data Science Institute, which is helping us set up the Zuse School until we have our own team in place. In parallel with the organisational activities, we now need to raise the Zuse School’s international profile and ensure that we can attract the right candidates.

What do you hope to gain in this context from your cooperation with the DAAD?

The DAAD is known worldwide for facilitating exchange between students and researchers, and has an established network of strong partners.  We believe that this will ensure a high profile for our Zuse School and allow us to recruit international talents.

The two partner universities, TUM and LMU, work together with both regional and highly prominent international partners in the Zuse School.

The network of cooperation partners is wide-ranging. It encompasses not only the Fraunhofer Institute for Cognitive Systems, the Fraunhofer Institute for Applied and Integrated Security, and Helmholtz Munich, but also AI centres from around the world, for instance at the universities of Stanford and Princeton. AI is a topic of global relevance and the research community is international. Exchange with the world’s leading universities is thus a key element within the school. Pursuing excellent research means networking internationally. Our participants are to be given the opportunity to do research stays with our partners. At the same time, we support international fellows who come to Germany and work as visiting scholars at the school, either doing research work or giving lecture series.

TUM is involved in two of the Zuse Schools – are there points of overlap, do you engage in exchange?

Even during the application phase it was always our goal to work together with the two other schools, jointly running workshops or fairs. TUM is also involved in the ELIZA School at TU Darmstadt, which likewise focuses on machine learning. We cooperate closely with our Munich colleagues from this department in any case, so there are bound to be aspects we can follow up on. Ultimately, however, it is not only about joint activities, but above all about making the schools generally known. Our joint objective is to perform on the international stage with these schools, promoting the advantages of Germany as a land of innovation. This is what matters to us, which is also why collaboration is important.

And what is the specific objective of the cooperation partners with respect to relAI?

The visibility of this topic and the success of participants are the top priority for us. On the one hand, we hope that Germany will acquire an international reputation for reliable AI, and that students will come to the school in Munich to pursue research in this field. For me, the second goal is for the participants to genuinely benefit from the school, to be successful, to establish a network thanks to all the partners we have on board, and to remain in contact with one another. To this end, we are also planning to set up an alumni network in the longer run. Successful graduates who work at top companies or become professors at good universities and are later willing themselves to contribute to the school have important boost potential. All of these are effects we want to achieve with the school.

Here you can find more information on relAI.

Interview: Gunda Achterhold

Web

www.zuseschoolrelai.de

Mail

coordinators@zuseschoolrelai.de

Post

Konrad Zuse School of Excellence in reliable AI
Munich Data Science Institute (MDSI)
Technical University of Munich
Walther-von-Dyck-Straße 10
85748 Garching, Germany

Konrad Zuse School of Excellence in reliable AI
Ludwig-Maximilians-Universität München
Akademiestraße 7
80799 Munich, Germany

Social Media

LinkedIn: /company/relai-konrad-zuse-school-of-excellence/
X: @zuseschoolrelAI