About this position
About the project
Intraoral scanners capture high-resolution 3D surface models of teeth and gingiva at almost every dental visit, yet their diagnostic content remains largely unused. In the FFG BRIDGE project ARTIS, we develop deep learning methods that derive dental status, gingival health and oral hygiene quality from these routine scans. Models are trained on scans from the University Clinic of Dentistry Vienna and validated in a prospective patient cohort, together with two industry partners from dental software.
What you will do
Develop deep learning models for 3D intraoral scans, starting from point cloud models such as PointNet++ and DGCNN
Extend these toward graph neural networks that operate directly on the mesh structure of the scans
Use explainability methods to show which geometric features drive each prediction
Validate the models with dentists and present your results at international conferences
Who we are looking for
If you have worked in 2D computer vision and want to move to 3D data, or have used graph neural networks in other domains, this project lets you apply these skills to clinical data. No prior experience with 3D data or dentistry is required.
Master's degree 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
Preferred, experience with graph neural networks, point clouds or meshes, PyTorch Geometric or explainable AI
What we offer
PhD position at the Medical University of Vienna, 30 hours per week, 36 months
Salary according to the collective agreement for Austrian universities (B1), currently EUR 2,832 gross per month, paid 14 times per year
A group of eight PhD students and a postdoc at the interface of machine learning and dentistry, based inside the dental clinic
Access to the MedUni Vienna high-performance computing cluster and travel funding for international conferences
How to apply
Send your CV, transcripts and a one-page motivation letter as a single PDF to
benedikt.sagl@meduniwien.ac.at
. Review of applications starts on November 23, 2026, and the position remains open until filled. Full details at
https://aidentvienna.org/positions/artis-phd/
MedUni Vienna is committed to diversity, inclusion and equal opportunity and explicitly encourages qualified women to apply.
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