PhD Student in Generative Acoustics in Surgery 100 %

The PhD student's primary responsibility is to conduct original research at the intersection of signal processing, machine learning, and medical imaging with direct application to orthopedic surgery. The student will focus on the development of generative audio models to create a realistic, action-conditioned soundscape of a surgical procedure and advance the multimodality of current surgical simulations. The work encompasses data collection through ex-vivo experiments, the development of novel scientific methods, their integration into a functional prototype, and its validation in user studies.

The work is expected to produce methodological contributions documented through publications in leading venues such as MICCAI, IPCAI, or Medical Image Analysis. During the initial phase, the candidate will review the state-of-the-art and establish a structured multi-year research plan together with the supervisor. The PhD is embedded in the graduate school of the University of Zurich, which also involves serving as a teaching assistant for 1–2 courses per year.

In particular, the PhD project will involve:

• Advancing the state-of-the-art of generative models to generate action-conditioned realistic soundscapes for the next generation of multimodal surgical simulations
• Developing an advanced data capture setup and acquiring a dataset in a realistic ex-vivo experiment at OR-X
• Benchmarking existing generative approaches to produce a realistic soundscape for surgical tool-tissue interaction
• Performing validation and user studies to evaluate the effectivness of the developed methods
• Collaborating with surgeons and engineers to ensure translational relevance
• Disseminating results through scientific publications, patents, and prototype demonstrations