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

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Your Responsibilities

The PhD student's primary responsibility will be 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, to develop methods that reconstruct and simulate anatomical changes during manipulation.

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

This work is expected to yield methodological contributions that will be 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 in collaboration with the supervisor. The PhD is embedded within the graduate school of the University of Zurich, which includes serving as a teaching assistant for 1-2 courses per year.

In particular, the PhD project will involve:

• 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 fields
• Exploring simulation approaches ranging from finite element simulations to learned-physical models
• Conducting user studies to evaluate the usefulness of developed methods in surgical training settings
• Collaborating with surgeons and engineers to ensure translational relevance
• Disseminating results through scientific publications, patents, and prototype demonstrations

Your Profile

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 possess excellent programming skills combined with experience in medical imaging and camera hardware.

We are seeking candidates who demonstrate:

• A solid understanding of generative models and proven experience in working with multimodal data
• A strong grasp of 3D geometry processing, reconstruction, and registration
• 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 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 (German is an asset), coupled with initiative, problem-solving ability, and teamwork skills

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