Postdoc – AI-Based Motion Monitoring for Gated CBCT-Guided Adaptive Radiotherapy

Scope of duties

In a multi-institutional research project funded by the German Research Foundation (DFG), a Postdoc position investigating the use of AI models for markerless lung tumor tracking during cone-beam CT (CBCT)-guided radiotherapy is open at the Department of Radiation Oncology of the LMU Munich University Hospital (Prof. Guillaume Landry & PD Christopher Kurz).

The project aims at developing state-of-the-art AI models for real-time, markerless tracking of lung tumors on continuously acquired CBCT projections (360° rotation) during irradiation. Focus will be set on developing patient-specific models that make use of available prior knowledge. The project also foresees validation on external CBCT data from our project partners at the University Hospital Cologne (PD Florian Kamp), as well as experimental validation with dedicated lung phantoms at MAASTRO Clinic.

The candidate is expected to :

• Develop state-of-the-art AI models for markerless lung tumor tracking on CBCT projections
• Investigate options for dose reduction by means of AI-based super-resolution
• Evaluate the developed tools on external data and perform phantom-based validation

Our requirements

Highly ranked PhD in Medical Physics, Biomedical Engineering, or Computer Science

Our offer

The working place will be at the LMU Klinikum Großhadern.

To apply for the position, please send your electronic application (letter of motivation, curriculum vitae, last school certificate and university degrees, PhD certificate, publication list, other qualification certificates such as TOEFL, and the contact information of two references), preferably in PDF format.

The successful candidate will work in a highly motivated and well-established research group (2 senior researchers, 2 postdocs, 10 PhD students, and MSc students) within a multidisciplinary and international network embedded in a stimulating scientific environment at LMU Munich, with a long tradition of collaboration and excellence in biomedical research and with outstanding research and clinical infrastructures.