Applications are invited for a fully funded four-year Doctoral Position in 3D Scene Reconstruction and Multi-Modal Sensing for Digital Twins of Stone-Built Heritage at ETH Zurich, Switzerland. The position is hosted by the Chair of Geosensors and Engineering Geodesy within the collaborative project “HC3 – Heritage Conservation Collaboration Copilots”.
Overview Table
| Detail | Information |
|---|---|
| Position / Designation | Doctoral Candidate (PhD Researcher) |
| Host Institution | ETH Zurich |
| Department / Chair | Chair of Geosensors and Engineering Geodesy (D-BAUG) |
| Project Name | HC3 – Heritage Conservation Collaboration Copilots |
| Research Area | Computer Vision, Geomatics, 3D Reconstruction & Remote Sensing |
| Location | Zurich, Switzerland |
| Employment Type | 100%, Fixed-term (4 Years) |
| Funding Status | Fully Funded |
| Application Deadline | Open until filled (Early application encouraged) |
Designation
Doctoral Candidate (PhD Student) – 100%
Research Area
Geomatics, Remote Sensing, Computer Vision, Robotics, Multi-Modal Data Fusion, 3D Scene Reconstruction (Point Clouds, NeRF, 3D Gaussian Splatting), Machine Learning, and Heritage Building Information Modeling (HBIM).
Location
ETH Zurich, Zurich, Switzerland.
Scholarship Description
The HC3 project aims to establish information-rich 3D digital twins of stone-built monuments that are interoperable through eXtended Reality (XR), supported by multi-modal sensing and monitoring data. This research helps model, understand, and predict stone-built heritage deterioration under climate change.
Key responsibilities include:
- Planning and conducting multi-sensor mapping campaigns of heritage monuments using ground-based and UAV-based LiDAR and imaging technologies.
- Calibrating, registering, and fusing point clouds and imagery into unified 3D representations (meshes, point clouds, neural radiance fields, 3D Gaussian splatting).
- Developing deep learning approaches for automated 3D scene parsing and element segmentation.
- Applying machine learning methods to infer stone deterioration and building material properties from multi-modal time-series data.
- Collaborating with material scientists and publishing in leading peer-reviewed journals and conferences.
Offer & Benefits:
- A fully funded four-year doctoral contract with competitive salary and benefits adhering to ETH Zurich salary scales.
- Access to cutting-edge sensing technology, computational resources, and world-class research infrastructure.
- An international, interdisciplinary, and supportive research environment in Zurich.
Eligibility / Qualifications
- An MSc degree (completed or near completion) in Geomatics, Remote Sensing, Computer Vision, Robotics, Computer Science, or a closely related discipline.
- Strong background in 2D/3D perception (photogrammetry, point cloud and image processing, 3D reconstruction) and parameter estimation/deep learning.
- Proficiency in programming with Python or C++.
- Hands-on experience with LiDAR systems or imaging hardware is an advantage.
- Willingness to undertake fieldwork on monuments.
- Strong interest in interdisciplinary collaboration with material scientists and conservation experts.
- Excellent proficiency in spoken and written English; knowledge of German or French is advantageous.
How to Apply
Candidates must submit their application exclusively via the ETH Zurich online portal. Applications sent by email or postal services will not be considered. Prepare the following documents in PDF format:
- Curriculum Vitae (CV), including a list of scientific publications (if applicable).
- Cover Letter containing:
- A brief summary (max. 1/2 A4 page + 1 figure) highlighting a previous project/result you are proud of that illustrates your relevant technical skills.
- A brief statement (max. 1/2 A4 page) outlining your research vision for this doctoral project.
- Contact information of two academic references.
- Copies of Master’s degree certificate/diploma and academic transcripts.
Last Date to Apply
Applications are reviewed on a rolling basis. The position remains open until filled. Prospective applicants are advised to submit their applications as soon as possible.
Apply Link
For more details and to submit your application, please visit the official job opening:
Apply Online at ETH Zurich Job Portal
