Postdoctoral Researcher (m/f/d)

Application deadline: 15/07/2026

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 University Hospital Würzburg, Institute of Diagnostic and Interventional Radiology (Prof. Dr. Tobias Wech), is seeking to fill the position of a Postdoctoral Researcher (m/f/d, E13, 100%) for this exciting project at the earliest possible date.

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 environment with experts in radiology, rheumatology, and AI

A stimulating academic environment with opportunities for further qualification

Attractive remuneration according to TV-L E13 (100%) 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

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

Supervision of PhD students and technical staff

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, 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 Profile

Completed PhD in Computer Science, Medical Engineering, Physics, Biomedical Engineering, or a related field

Strong background in machine learning and deep learning, particularly for image analysis

Extensive expe