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

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Your Responsibilities

The primary responsibility of the PhD student will be to conduct original research focused 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, to develop methods for reconstructing and simulating anatomical changes during manipulation.

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

This role is expected to yield methodological contributions, which will be documented through publications in leading venues, such as MICCAI, IPCAI, or Medical Image Analysis. In the initial phase, the candidate will conduct a review of the state-of-the-art and collaborate with their supervisor to establish a structured multi-year research plan. 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.

Specific Project Involvements Include

• 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 for tracking tool-tissue interactions.
• Investigating learning-based methods for dynamic anatomy modeling, including GNNs, implicit neural representations, and radiance fields.
• Exploring simulation strategies including finite element simulation and learned-physical models.
• Conducting user studies to evaluate the practical applications of the developed methods in surgical training.
• Collaborating with surgeons and engineers to ensure the translational relevance of research outcomes.
• Disseminating findings through scientific publications, patents, and prototype demonstrations.

Your Profile

You hold an excellent MSc degree in computer science, robotics, or electrical engineering, complemented by a robust background in computer graphics, simulation, and computer vision. You combine outstanding programming skills with practical experience in medical imaging and related camera technologies.

We seek candidates who demonstrate:

• A solid understanding of generative models and proven experience working with multimodal data.
• A strong grasp of 3D geometry processing, reconstruction, registration, or scene analysis.
• 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 (e.g., finite elements, mass-spring models, or physics-informed models) is a plus.
• Practical experience with computer vision, camera calibration, and tracking, along with proficiency in relevant libraries (e.g., OpenCV, Open3D).
• Familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow) and medical image analysis libraries (Slicer3D, MONAI).
• Excellent communication skills in English (knowledge of German is a plus), combined with initiative, problem-solving ability, and a strong sense of teamwork.

Apply online using the form below. Please note that only applications matching the job profile will be considered.

Contact Information

Joelle Kunz
Assistant
+41 44 510 73 66
Joinrocs@balgrist.ch