Master Theses

Here you find an overview about our currently available master theses. If you have not found a topic that interests you, but are interested in writing your thesis at IMRG, please direct contact Prof. Dr. Schwabe

Please, keep in mind that thesis submitted in August and February will not be corrected before semester degree conferral dates!

Further information about the MSc program can be found at Master in Informatics

Career planning tasks, such as CV updating, networking, or job opportunities researching, are important for a successful entrance to the future career. Yet, they often fall into the “important but not urgent” trap, leading to procrastination. This can cause students to miss key deadlines and face overwhelming stress when graduation approaches. We are looking for a motivated student to investigate how a generative AI-powered system can help students and career advisors overcome this specific type of procrastination. read more

Cognitive biases are widespread among citizens, and Switzerland is no exception. Common biases such as confirmation bias, motivated reasoning, and groupthink not only influence structured discussions, but also hinder the democratic process. We are looking for a motivated student to investigate how generative AI could help identify and reduce cognitive biases within the scope of persuasive AI. read more

As part of a research project with SCROBS, we offer a master's thesis opportunity to explore the integration of a voice-controlled, AI-based robotic assistant into surgical workflows. SCROBS is an innovative start-up aimed at addressing the increasing personnel shortage in hospitals by developing a robot that supports surgical teams. The system is designed to adapt to the highly specialized and dynamic environment of operating rooms, ensuring hygiene, precision, and usability.  read more

AI is developing rapidly and offers enormous opportunities to increase efficiency and effectiveness, not only for companies but also for public administration. However, AI also poses a major challenge. Business processes, employees' working methods and their attitudes need to be considered in order to identify these. In a vignette study on personnel selection with AI, without AI and with AI, 120 HR administrators and 80 managers with personnel responsibility in public administration were surveyed. The aim was to find out under which aspects they would use AI in the personnel selection process and which aspects stand in the way of its use. read more

Many AI systems contain biases that are built into the data or the models themselves. These biases can be reduced but are often impossible to remove completely. In practice, organizations may still deploy such systems, sometimes consciously accepting a certain level of bias, and other times without fully recognizing it. This thesis will explore this bias tolerance, i.e. how much inherent AI bias organizations are willing or unwillingly prepared to accept, and examine the factors influencing these decisions and their implications for governance and trust. Interested? Contact Liudmila Zavolokina