PhD Position (m/f/d)

  • Uniklinikum Würzburg - Institut für Diagnostische und Interventionelle Radiologie
  • 01.09.2026
  • Teilzeit
  • befristet

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).

Wir bieten

  • 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

Ihr Aufgabengebiet

  • 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.

Ihr Profil

  • Master’s degree in Computer Science, Medical Informatics, Physics, Engineering, or a related field
  • Experience in programming (e.g., Python) and relevant ML frameworks (e.g., PyTorch, TensorFlow)
  • Basic knowledge of MRI data processing and medical image analysis is a plus
  • Interest in semantic segmentation and computer vision techniques
  • Excellent written and spoken English
  • Independent, structured working style and strong problem-solving skills
  • Ability to work in an interdisciplinary team environment

Darauf können Sie sich freuen

  • Anspruchsvolles, vielfältiges und entwicklungsfähiges Aufgabengebiet
  • Attraktive Bezahlung nach TV-L inkl. Jahressonderzahlung
  • Aus- und Weiterbildung in der eigenen Akademie
  • Betriebliche Altersvorsorge
  • Betriebskindertagesstätte mit verlängerten Öffnungszeiten
  • Betriebssportangebote
  • Flexible Arbeitszeiten dank Gleitzeitregelung
  • Jobrad
  • Mitarbeiter Angebote

Werden Sie Teil des Teams: Jetzt bewerben!

Prof. Dr. Tobias Wech
Professor für Experimentelle Radiologie
Tel: +49931 201 46356


Die Vergütung erfolgt nach den einschlägigen Tarifverträgen. Schwerbehinderte Bewerber/-innen werden bei ansonsten im Wesentlichen gleicher Eignung bevorzugt eingestellt.