Join the University of Zurich: An Opportunity Awaits
The University of Zurich, Switzerland's largest university, provides a diverse array of attractive positions across various disciplines and professional fields. With approximately 10,000 employees and 12 professional apprenticeship streams, the University fosters an inspiring working environment focused on cutting-edge research and exceptional education. Put your talents and skills to work with us! Learn more about UZH as an employer.
Your Responsibilities
As a PhD student, your primary responsibility will be to conduct innovative research on dynamic 3D modeling of surgical anatomy under tool-tissue interaction. You will utilize 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.
Core research challenges will encompass:
- 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, as well as advanced differentiable rendering frameworks like Gaussian Splatting.
- Defining the exact direction of the project through scientific exploration.
The work is anticipated to yield significant methodological contributions, documented through publications in prestigious venues such as MICCAI, IPCAI, or Medical Image Analysis. Initially, the candidate will review the state-of-the-art and collaboratively establish a structured multi-year research plan with the supervisor. This PhD position is part of the University of Zurich's graduate school and requires serving as a teaching assistant for 1-2 courses each year.
PhD Project Involves
- 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.
- Exploring learning-based methods for dynamic anatomy modeling, including GNNs, implicit neural representations, and radiance fields.
- Investigating simulation approaches ranging from finite element simulations to learned-physical models.
- Conducting user studies to evaluate the effectiveness of developed methods in a surgical training environment.
- Collaborating with surgeons and engineers to ensure translational relevance.
- Disseminating results through scientific publications, patents, and prototype demonstrations.
Your Profile
The ideal candidate holds a distinguished MSc degree in computer science, robotics, or electrical engineering, with a robust background in computer graphics, simulation, and computer vision. You will bring strong programming skills, combined with experience in medical imaging and camera hardware.
We seek candidates who demonstrate the following:
- A solid understanding of generative models and proven experience with multimodal data.
- Strong knowledge of 3D geometry processing, reconstruction, registration, or scene representation.
- 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 an asset.
- Practical experience with computer vision, camera calibration, and tracking, as well as 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 advantage), along with initiative, problem-solving capabilities, and teamwork.
If you are interested in this opportunity, 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