Image registration is an essential step in many clinical applications, such as image guidance, motion tracking, image segmentation, and treatment-response assessment. In radiotherapy, registration of the patient's computed tomography (CT) and/or magnetic resonance (MR) images is key to enable accurate contour propagation and dose accumulation. Traditional algorithms for medical image registration often achieve good results but suffer from being very computationally expensive. Deep learning-based methods, on the other hand, can perform registration in a single shot, thereby enabling real-time registration. While demonstrating astonishing results in small-scale simulation studies, the use of deep learning to register high-resolution 3D images in clinical settings remains an open challenge.

This research project focuses on developing novel deep learning algorithms, data synthesis techniques and training strategies for medical image registration. During the project, you will be able to apply your developed techniques to solve relevant problems in adaptive radiotherapy. In this field, fast and accurate registration of cone-beam CT images is crucial to facilitate personalized radiotherapy treatments on a daily basis, in which the dose is delivered conformal to the tumor while increasing the sparing of healthy tissue.

The project will be supervised by Dr. Maureen van Eijnatten, Assistant Professor 'Artificial Intelligence for Medical Imaging' at TU/e. During the project you will closely collaborate with clinicians and researchers of the University Medical Center Utrecht (Department of Radiotherapy). In addition, the project builds upon existing collaborations with the University of Cambridge (UK) and the Department of Radiology of Addenbrooke's hospital in Cambridge. These collaborations also offer opportunities to spend some time abroad as a visiting researcher at the University of Cambridge.

The successful candidate will become a member of the Medical Image Analysis Group at the Department, headed by Prof. Josien Pluim. The group consists of around 20 enthusiastic researchers, working on both methodological and applied innovations. Research topics include image analysis, quantification and machine/deep learning for oncology, cardiology, neurology and histopathology, as well as high-field MR imaging and RF safety. The group has strong ties with the University Medical Center Utrecht (both in research and education) and Philips NL, but also collaborates with other clinical institutes and industry.

• A master's degree (or an equivalent university degree) in Physics, Computer Science, Mathematics, Electrical Engineering, Biomedical Engineering/Technology or a related discipline
• Good programming skills in Python
• Preferably experience both in deep learning and in medical imaging

We are looking for candidates who have:

• The ability to contribute to cross-disciplinary collaborations
• A research-driven and proactive attitude
• Excellent written and oral communication skills in English

TU/e aims to increase diversity among its employees and encourages applications from under-represented groups; in particular, female scientists are encouraged to apply.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation after one year.
• To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.