Medical University of Vienna
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PhD Position in 3D Deep Learning for Dental Diagnostics Medical University of Vienna in Austria
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Jan 15, 2027
Country
Austria
University
Medical University of Vienna

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About this position
PhD position in 3D Deep Learning for Dental Diagnostics at the Medical University of Vienna, based in the University Clinic of Dentistry in Vienna, Austria.
This project is part of the FFG BRIDGE project ARTIS and focuses on using routine intraoral scans to derive clinically useful information such as dental status, gingival health, and oral hygiene quality. The research combines 3D deep learning, point cloud learning, graph neural networks, mesh-based representation learning, and explainable AI in a clinically relevant setting. You will work with high-resolution 3D surface models of teeth and gingiva collected from the University Clinic of Dentistry Vienna and validated in a prospective patient cohort, in collaboration with two industry partners from dental software.
The role includes developing deep learning models for 3D intraoral scans, starting with architectures such as PointNet++ and DGCNN, extending to graph neural networks that operate directly on scan meshes, and applying explainability methods to identify the geometric features behind each prediction. The successful candidate will validate models with dentists and present findings at international conferences.
The position is well suited to applicants with a background in computer science, data science, mathematics, physics, biomedical engineering, or a related field who have strong experience in deep learning, Python, and PyTorch (or equivalent). Prior experience with 3D data or dentistry is not required, and the advert explicitly welcomes candidates with 2D computer vision experience who want to move into 3D data. Preferred experience includes graph neural networks, point clouds or meshes, PyTorch Geometric, and explainable AI.
The appointment is a PhD position, part-time at 30 hours per week for 36 months. Funding is provided through the project, with salary according to the Austrian universities collective agreement (B1), currently EUR 2,832 gross per month paid 14 times per year. Additional support includes access to the MedUni Vienna high-performance computing cluster and travel funding for international conferences.
Applications should be submitted by email as a single PDF containing a CV, transcripts, and a one-page motivation letter. Review of applications starts on 2026-11-23, and the position remains open until filled. The stated deadline is 2027-01-15. MedUni Vienna states a commitment to diversity, inclusion, and equal opportunity and encourages qualified women to apply.
Funding details
Available
What's required
Master's degree or equivalent in computer science, data science, mathematics, physics, biomedical engineering, or a related field. Solid experience with deep learning, Python, and PyTorch or a similar framework is required. Preferred experience includes graph neural networks, point clouds or meshes, PyTorch Geometric, or explainable AI. No prior experience with 3D data or dentistry is required; candidates with 2D computer vision experience moving into 3D data are welcome.
How to apply
Send your CV, transcripts, and a one-page motivation letter as a single PDF to [email protected]. Review of applications starts on 2026-11-23 and the position remains open until filled. Use the provided email subject if possible and refer to the full details on the project page.
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