PhD Candidate in Dynamic Soft Tissue 3D Reconstruction and Simulation / PhD Candidate in Dynamic Soft Tissue 3D Reconstruction and Simulation

University of Zurich - Job Opportunity

The University of Zurich, Switzerland's largest university, offers a variety of attractive positions across numerous subject areas and professional fields. With approximately 10,000 employees and 12 professional apprenticeship streams, the University creates an inspiring work environment dedicated to cutting-edge research and top-tier education. Put your talent and skills to good use with us!

Your Responsibilities

The PhD student's primary responsibility involves conducting original research on dynamic 3D modeling of surgical anatomy under tool-tissue interaction. You will engage with multi-modal data acquired at OR-X, incorporating CT scans, 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 the accurate 3D reconstructions that can be continuously updated from sensor data. Potential approaches may involve graph neural networks, NeRF representations, point-based and diffusion-based models, as well as advanced differentiable rendering frameworks like Gaussian Splatting. The precise direction of the project will evolve based on scientific explorations.

Expected contributions include methodological advancements documented through publications in prominent venues such as MICCAI, IPCAI, or Medical Image Analysis. Initially, the candidate will review the state-of-the-art and collaborate with the supervisor to establish a structured multi-year research plan. This PhD position is part of the graduate school at the University of Zurich, which includes serving as a teaching assistant for 1-2 courses per year.

Your involvement in this PhD project will encompass:

• Building multi-modal 3D reconstructions by merging 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 methods ranging from finite element simulations to learned physical models.
• Conducting user studies to evaluate the effectiveness 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

We are seeking candidates with an outstanding MSc degree in computer science, robotics, or electrical engineering, complemented by a strong background in computer graphics, simulation, and computer vision. The ideal candidate will possess excellent programming skills and experience in medical imaging and camera hardware.

We are particularly interested in candidates who demonstrate the following:

• A solid understanding of generative models with proven experience in handling multimodal data.
• A strong grasp of 3D geometry processing, reconstruction, registration, or scene understanding.
• 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, including finite elements, mass-spring models, or physics-informed models, is an advantage.
• 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 (e.g., Slicer3D, MONAI).
• Excellent communication skills in English (German is a plus), along with initiative, problem-solving abilities, and a collaborative mindset.

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

For inquiries, please contact:

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