PhD Student In Surgical Tool–Tissue Modeling, 3D Reconstruction, And Soft Tissue Simulation - S

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

Universität Zürich, Zürich. The Research in Orthopedic Computer Science (ROCS) group at the University of Zurich develops advanced solutions for surgical context understanding, training, guidance, and robotics. Our work combines medical imaging, computer vision, and machine learning with strong clinical translation, in close collaboration with Balgrist University Hospital and the national research platform OR‑X.

We are offering a PhD position in a Swiss National Science Foundation project on Surgical Digital Twins. The PhD project focuses on modeling how surgical tools interact with bone and soft tissues, reconstructing these interactions from multi‑modal sensor data, and generating dynamic, high‑fidelity 3D representations suitable for both scientific analysis and interactive replay. The research will span 3‑D scene reconstruction, physics‑aware modelling, and data‑driven deformation learning.

This position provides a unique opportunity to work at the frontier of computer‑assisted orthopedic surgery, embedded in Zurich's vibrant Med Tech and AI ecosystem. With direct access to the state‑of‑the‑art OR‑X infrastructure for translational validation, the successful candidate will join an innovative team and collaborate with surgeons and industry partners to shape technologies with strong potential for clinical adoption.

• Conduct original research on dynamic 3D modelling of surgical anatomy under tool–tissue interaction using multi‑modal data acquired at OR‑X, including CT, RGB‑D sequences, tool tracking, and optical surface models.
• Build methods that reconstruct and simulate anatomical changes during manipulation, continuously updating 3‑D reconstructions from sensor data with approaches such as graph neural networks, NeRF representations, point‑based and diffusion‑based models, and advanced differentiable rendering frameworks such as Gaussian Splatting.
• Produce methodological contributions documented through publications in leading venues such as MICCAI, IPCAI, or Medical Image Analysis.
• Review the state‑of‑the‑art and establish a structured multi‑year research plan together with the supervisor.
• Serve as a teaching assistant for 1–2 courses per year as part of the graduate school of the University of Zurich.
• Perform user studies to evaluate the usefulness of the developed methods in a surgical training setting.
• Collaborate with surgeons and engineers to ensure translational relevance.
• Disseminate 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.

• Solid understanding of generative models and proven experience in working with multimodal data.
• Strong understanding of 3‑D geometry processing, reconstruction, registration, or scene modelling.
• Experience with modern 3‑D learning paradigms such as graph neural networks, implicit neural representations, or point/voxel‑based models.
• Familiarity with physical simulation concepts or deformable modelling (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.

Our employees benefit from a wide range of attractive offers.