Interdisciplinary graduate school

The Zuse School SECAI is an interdisciplinary graduate school run by the TU Dresden in cooperation with Leipzig University and the University Hospital Carl Gustav Carus in Dresden. Students and researchers from the fields of computer science, microelectronics, medicine and law work together on new transdisciplinary approaches. The objective is to explore hybrid methods and algorithms that combine the strengths of various AI methods (composite) and to integrate these algorithms into specially designed microelectronics and intelligent devices (embedded).

Master’s and PhD funding

The Zuse School SECAI awards two-year scholarships to master’s and diploma students in degree courses with relevance to AI, such as nanoelectronic systems, computational modelling and simulation, data science, bioinformatics and medical informatics. A three-year PhD scholarship is likewise offered, as is a two-year training programme for doctors undergoing specialist training (clinical scientists). Research focuses on areas including composite AI, AI compute paradigms, intelligent medical devices, AI methods for health and societal framework for AI.

Supervision by academic fellows

One central element of the school is the concept of supervision by academic fellows. This applies equally to master’s students and doctoral candidates. Among other things, the SECAI Café provides a regular opportunity for exchange between students and researchers regarding questions about their studies, their research or their career plan. In addition, scholarship holders are integrated into research groups relevant to their subject areas, with professors acting as mentors. Doctoral candidates are each supervised by two academic fellows, giving them the chance to participate in two different research groups and thereby fostering interdisciplinarity. Further types of support, such as a tandem mentoring programme and funding for a study-related project, can be found here.
 

Outstanding mentoring

The 25 academic fellows form the core element of the school and work together to achieve the scientific goals and to pursue teaching and research. The fellows include some of the most renowned international AI researchers, such as Sayan Mukherjee (research group leader at the Max Planck Institute for Mathematics in the Sciences in Leipzig and holder of an Alexander von Humboldt Professorship) and Sebastian Rudolph (professor for computational logic at the TU Dresden and holder of an European Research Council (ERC) grant).

Funding for underrepresented groups

Some of the master’s scholarships are specifically aimed at female students in STEM subjects. SECAI is generally committed to promoting women and other disadvantaged groups. When selecting candidates for doctoral positions, the compatibility of family and career has a high priority.

Applications are submitted directly to the Zuse School.

Master’s 

German and international students can apply for a two-year scholarship from the graduate school SECAI if they are currently studying for a master’s degree at the TU Dresden or at Leipzig University, have an excellent academic track record, and if the content of their studies is closely related to the areas of SECAI research focus. Scholarship applications are separate from applications to degree courses at the universities in Leipzig and Dresden. Dedicated calls for scholarship applications are advertised each year, and speculative applications are also possible.

Bachelor’s and master’s students from other universities can also apply for a grant to finance a stay of up to six months in Dresden or Leipzig. Further information about a master’s scholarship and the application process can be found here. Funding for master’s students normally starts at the beginning of the winter semester each year in October.

PhD

Master’s graduates can apply for the three-year PhD programme if they have an excellent university degree in a discipline relevant to the SECAI research areas (which include computer science, mathematics, electronics, medical technology, bioinformatics and law), as can doctors undergoing specialist training (clinical scientists). Each spring, SECAI selects particular topics from its research areas and invites researchers to apply. Ideally, doctoral candidates and clinical scientists should begin their PhD in September each year. Would you like to know which doctoral candidates and clinical scientists are currently studying at the SECAI graduate school? Find an overview here.
 

Research at SECAI focuses on areas in which electronics, computer science and medicine overlap.

Composite AI

In the focus area Composite AI, researchers explore the extent to which hybrid methods and algorithms can combine the strengths of different AI approaches.

AI Compute Paradigms

The aim of the focus area AI Compute Paradigms is to develop fundamentally new computing hardware and to study its effective use in AI.

Intelligent Medical Devices

Intelligent Medical Devices focuses on cyber-medical AI systems and clinically embedded AI applications.

AI Methods for Health

The focus area AI Methods for Health encompasses research on AI methods for biomedical data analysis and biomedical knowledge management.

Societal Framework for AI

The Societal Framework for AI focus area aims to pursue cross-cutting research encompassing a wide range of societal interests, aspects relating to guidelines and higher-level principles.
 

SECAI is a TU Dresden consortium in cooperation with Leipzig University and the University Hospital Carl Gustav Carus in Dresden. The school also collaborates closely with selected partner organisations in research and industry. The cooperation with academic partners aims to foster exchange between students and joint research, while industry partners and start-ups offer support by establishing contact with practitioners and opening up additional career paths.

Academic partners

In the academic domain, SECAI is currently cooperating with Carnegie Mellon University (USA), École normale supérieure, PSL (France), King’s College London (United Kingdom), Technische Universität Wien (Austria), University of Cape Town (South Africa), University of Wrocław (Poland), Centre for Artificial Intelligence Research (CAIR) (South Africa).

Industry partners

Industry partners currently include IBM, Infineon, Siemens, Siemens Healthineers, Unite, Zeiss. In addition, SECAI cooperates with the following start-ups: Campus Genius, Cellcopedia, Condon, Mediainterface, Meshmerize, Mimetic, Navigo, Spinncloud, Wandelbots Teaching.

 

Though there are many potential areas of application for artificial intelligence (AI) in medicine, its use raises not only technical but also legal and ethical questions. Professor Markus Krötzsch, project coordinator at the SECAI school and group leader at the International Center for Computational Logic of TU Dresden’s Faculty of Computer Science, explains what makes this topic so fascinating.

Professor Krötzsch, what sets SECAI apart as an excellent graduate school for budding AI experts?

The combination it offers is unique. With our cooperation partner Leipzig University and the university hospital here in Dresden we are able to explore specific AI applications in greater depth than is possible elsewhere. At both sites, excellent Zuse School fellows are researching aspects of AI that go far beyond machine learning. I’m talking about things like symbolic knowledge-based processes that can be seen as complementing data-driven research. What sets us apart is the way we link symbolic methods with approaches to statistical machine learning, which requires close collaboration between experts in these complementary fields – these composite aspects are an important element of our work.

WWhat is meant by “symbolic AI”?

Symbolic processes provide a concrete representation of AI problems and their solutions, in the form of a digital model of the world that humans can intuitively understand thanks to the conceptual way in which it is structured. By contrast, machine learning often involves just millions of numbers that have no obvious meaning. The most successful systems tend to combine both approaches. At SECAI we are interested above all in symbolic representations of human knowledge – specialist medical knowledge, for example – and ask how this can be systematically combined with machine learning.

Another focus at SECAI is on embedding AI algorithms in microelectronics and smart medical devices. Can you explain what this involves?

In this second focal area, which we term “embedded”, hardware plays a crucial role. It is a question of making artificial intelligence physically “tangible”, of developing chips and accelerating processes. Dresden is Europe’s largest chip production site; indeed even worldwide there are very few universities that can pursue electronics research at this level. This encompasses everything from building a supercomputer to small integrated circuits such as those used in implants and medical technology.

How was the school’s specialist area chosen?

Medicine is an important area of application because it has social relevance while at the same time being extremely broad-ranging and diverse. Bioinformatics, which aims to develop personalised medicine, is one such area, as are smart cardiac pacemakers or monitoring systems that can be attached to patients in order to identify risks.

How can AI improve medicine?

In the area of intelligent data processing, we are trying to understand better, more quickly and more individually how to help the patient – personalised medicine and diagnostics are the buzzwords here. The main area of application involves so-called cybermedical systems. These are systems that can interact flexibly with the physical world, for example by using sensors to assess a patient‘s current state, injecting drugs and regulating dosages. They have software components to control the entire process, forward data and provide monitoring functions that enable medical staff to react swiftly in an emergency.

What do the cooperation partners hope to achieve with SECAI?

One reason why we find this DAAD programme so attractive and interesting is that it contains a scholarship component that benefits students even at master’s level. After all, one of our goals is to improve training in AI and to introduce students at an early phase to the new technologies and research fields so that they can then apply their knowledge in industry or research. A second point is that we ourselves want to continue to make our contribution to cutting edge research, and see SECAI in some way as the “glue”: our aim is to pool the considerable individual achievements of the participating project partners and their expertise to create something bigger, and to combine these initiatives and expand synergies.

What support, besides funding, do you see the DAAD as providing?

The DAAD has a very effective network that allows us to reach students in other countries. People actually read the job vacancies and scholarship offers that the DAAD posts on its website. Furthermore, the DAAD has regional offices in many countries, seeks cooperation with higher education institutions there, gives lectures and organises career fairs – these are channels that would not otherwise be available to us. The opportunity to share this network was a great motivation for us.

How are student support and supervision arranged at SECAI?

Applicants have to choose an area in which they wish to work – in electronics for example, or on legal issues. We have published a list of doctoral topics that generally involve several different departments. Some of these are interdisciplinary, while others concern just one discipline but different sites. In other words, students will be in direct personal contact with various people from the outset who then act as their mentors. This approach is also important in terms of the cooperation between the project partners, as the doctoral students themselves are the linking element. That’s why we make sure that every student takes part in several groups.

What role is played by the linking of science and industry?

We know from experience that international students in particular are greatly interested in going into industry. It is difficult for them to find a suitable position, however. We see similar problems when students come to the end of their studies. It is not easy for them to pick a topic for their master’s thesis that combines their academic objectives with practical relevance. Many companies have R&D departments that are pursuing exciting AI projects, yet it is not so easy for students on the outside to see what work is taking place or who best to contact. SECAI gives us the chance to work with longstanding partners so that this exchange takes place smoothly.

AI is one of the mega topics of our time. What makes it such an attractive subject for students?

One needs to be able to see the big picture. Rather than being an individual technology, AI is defined by its area of application. Almost any technology that is developed in the field of computer science can be part of an AI system. To work in such a holistic discipline, it is important not to think only along a single track – and many young people, men and women alike, find this fascinating. As a university, however, we have to find a way to turn general enthusiasm for AI into actual specialist interest that will then produce the expertise that we need. We must maintain a balance between enthusiasm and a concrete subject profile so that students actually know what they are letting themselves in for. It is no use to us if we have lots of candidates who all want to enrol but then find themselves unable to cope with the course requirements. That is why we at SECAI also help applicants to understand the various courses on offer, and engage in PR activities relating to AI.

Interview: Gunda Achterhold

Web

www.secai.org

Mail

Coordination Office Dresden: 
secai-office@tu-dresden.de
Coordination Office Leipzig: 
laderick@bioinf.uni-leipzig.de

Post

SECAI Office Dresden
Technische Universität Dresden
Faculty of Computer Science
Institute of Theoretical Computer Science
Chair of Knowledge-Based Systems
01062 Dresden
GERMANY

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