PhD Position (m/f/d)

Application deadline: 15/07/2026

Contribute to AI research in vascular imaging and shape the future of Giant Cell Arteritis (GCA) diagnosis!

The University Hospital Würzburg offers you the opportunity to actively contribute to the DFG-funded project 'Artificial Intelligence-Assisted Diagnosis of Giant Cell Arteritis'.

Giant Cell Arteritis (GCA) is a systemic vasculitis that can lead to severe complications, including vision loss and stroke, if left untreated. This project aims to develop a machine learning-driven automatic diagnostic tool to robustly detect and evaluate GCA in magnetic resonance images, thereby enhancing diagnostic accuracy and preventing GCA-related complications.

The Institute of Diagnostic and Interventional Radiology (Prof. Dr. Tobias Wech) is seeking to fill the position of a PhD Student (m/f/d, 65% TV-L) for this exciting project at the earliest possible date. The position includes the opportunity for a PhD or Dr. rer. nat. at the Graduate School of Life Sciences (GSLS).

We offer

The opportunity to work in an innovative, multidisciplinary DFG-funded research project with high clinical impact

The chance to develop cutting-edge AI methods for a clinically relevant application

Access to unique multi-center GCA MRI datasets and state-of-the-art computational resources

Presentation of your research at prestigious international conferences

A multidisciplinary team with experts in radiology, rheumatology, and AI

A stimulating academic environment with opportunities for further qualification

PhD or Dr. rer. nat. at the Graduate School of Life Sciences (GSLS)

Attractive remuneration according to TV-L E13 (65%), including annual bonus

Your Tasks

Development and implementation of machine learning algorithms for automated detection and segmentation of GCA in MRI scans

Pre-processing of multi-center GCA MRI datasets (2D T1-weighted and 3D CS-SPACE sequences) using super-resolution models and brain stripping

Training and evaluation of neural networks for semantic segmentation to identify inflamed vessels in extra-cranial arteries

Development of quantitative imaging biomarkers (e.g., vessel wall thickness, volume of inflamed segments) for disease activity assessment

Validation of methods using multi-center datasets from collaborating GCA centers (Freiburg, Ludwigshafen, Aarau)

Collaboration with clinical partners to ensure clinical relevance and applicability of developed tools

Publication of results in peer-reviewed journals

Presentation of research findings at international scientific conferences (e.g., ISMRM, RSNA, ECR)

Project Background

Giant Cell Arteritis (GCA) is a systemic vasculitis primarily characterized by inflammation of medium and large vessels, with a predilection for superficial cranial arteries (temporal, ophthalmic) and large intrathoracic vessels. If left untreated, GCA can lead to severe complications, including vision loss, stroke, aortic aneurysms, or dissection.

While MRI has emerged as a pivotal technique for comprehensive GCA imaging, the interpretation of advanced imaging protocols remains challenging. This project addresses these challenges by developing AI-driven automatic diagnosis to enhance diagnostic accuracy, efficiency, and standardization.

Building on a decade of GCA research and a unique data archive, we aim to create a machine learning-driven diagnostic tool that can be distributed free-of-charge to non-GCA centers, ensuring wide accessibility and improving patient outcomes worldwide.

Your P