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

This thesis is embedded in an applied research collaboration between UZH, ZHAW (Prof. de Spindler) and an industry partner. The project explores a new category of digital co-facilitation that helps teams reflect during a conversation. A prototype of a so called “Clarity and Insight” module will be developed and should be evaluated as part of the thesis. This module transcribes live discussions, identifies blind spots, tracks convergence or divergence, and provides micro interventions that support shared understanding. read more

Many software developers use generative AI to support their work. However, currently everyone does this individually. In collaboration with the software company Abraxas, we are investigating how this support with GenAI can be generalized so that digital agents can be used by multiple developers. To this end, the current use of GenAI in software development at Abraxas will be examined and developed with a focus on digital agents. 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

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.read more