PhD Student in Surgical Tool-Tissue Modeling, 3D Reconstruction, and Soft Tissue Simulation

The University of Zurich, Switzerland's largest university, offers a range of attractive positions in various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams the University offers an inspiring working environment on cutting-edge research and top-class education. Put your talent and skills to work with us. Find out more about UZH as an employer! Your responsibilities

The PhD student's primary responsibility is to conduct original research on dynamic 3D modeling of surgical anatomy under tool–tissue interaction. You will work with multi-modal data acquired at OR-X, including CT, RGB-D sequences, tool tracking, and optical surface models, and use them to build methods that reconstruct and simulate anatomical changes during manipulation.

The core research challenges include accurate 3D reconstructions that can be continuously updated from sensor data. Potential approaches include graph neural networks, NeRF representations, point-based and diffusion-based models, and advanced differentiable rendering frameworks such as Gaussian Splatting. The exact direction will be shaped based on the project's scientific explorations. n 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.

• Building multi-modal 3D reconstructions by fusing CT-based anatomical models with photorealistic or depth-based surface reconstructions from optical cameras
• Developing algorithms to integrate real-time camera observations into dynamic anatomical models to track tool–tissue interactions
• Investigating learning-based methods for dynamic anatomy modeling, including GNNs, implicit neural representations, and radiance-fiel
• Exploring simulation approaches ranging from FE simulation to learned–physical models
• Performing user studies to evaluate the usefulness of the developed methods in a surgical training setting
• Collaborating with surgeons and engineers to ensure translational relevance
• Disseminating results through scientific publications, patents, and prototype demonstrations

You hold an excellent MSc degree in computer science, robotics, or electrical engineering with a strong background in computer graphics, simulation, and computer vision. You combine excellent programming skills with experience in medical imaging and camera hardware.

We are looking for candidates who demonstrate:

• Solid understanding of generative models and proven experience in working with multimodal data
• Strong understanding of 3D geometry processing, reconstruction, registration, or scene
• Experience with modern 3D learning paradigms such as graph neural networks, implicit neural representations, or point/voxel-based models
• Familiarity with physical simulation concepts or deformable modeling (finite elements, mass–spring models, or physics-informed models) is an asset
• Practical experience with computer vision, camera calibration, and tracking, and proficiency in relevant libraries (e.g., OpenCV, Open3D)
• Familiarity with AI/ML frameworks (e.g., PyTorch, Tensor Flow) and medical image analysis libraries (Slicer3D, MONAI)
• Excellent communication skills in English (German is an asset), combined with initiative, problem-solving ability, and teamwork

Joelle Kunz
Assistant

Joinrocs