PhD Candidate in Dynamic Soft Tissue 3D Reconstruction and Simulation

The University of Zurich

As Switzerland's largest university, the University of Zurich offers a range of attractive positions across various subject areas and professional fields. With approximately 10,000 employees and 12 professional apprenticeship streams, we provide an inspiring work environment grounded in cutting-edge research and top-tier education. Put your talents and skills to work with us and discover more about UZH as an employer!

Your Responsibilities

The primary responsibility of the PhD student 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, to develop methods that reconstruct and simulate anatomical changes during surgical manipulation.

The core research challenges involve achieving accurate 3D reconstructions that can be continuously updated from sensor data. Potential approaches may include:

• Graph neural networks
• NeRF representations
• Point-based and diffusion-based models
• Advanced differentiable rendering frameworks, such as Gaussian Splatting

The specific direction of the research will be tailored through scientific explorations and user studies.

Your work is expected to lead to 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 in collaboration with the supervisor. The PhD program is integrated into the graduate school of the University of Zurich, which entails 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 simulation to learned-physical models
• Performing user studies to evaluate the usefulness of the 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

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

We are looking for 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, and scene analysis
• Experience with modern 3D learning paradigms such as graph neural networks, implicit neural representations, and 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 in computer vision, camera calibration, tracking, and 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), combined with initiative, problem-solving ability, and teamwork

Application Process

To apply online, please use 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