PhD candidate 'Deep Learning Based Image Registration for Medical Images'

This PhD opportunity involves developing an advanced deep-learning-based image registration framework for medical applications, with a focus on enhancing lesion tracking during cancer treatment and exploring broader medical imaging uses. Are you the PhD candidate who will join our ambitious team?

Image registration is the process of aligning two or more images to achieve pointwise spatial correspondence. This step is crucial in medical image analysis as it enables the integration of previously unrelated data, allowing for joint processing of information. By establishing spatial correspondences between multiple images, it aligns anatomical structures and facilitates the automatic quantification of findings in subsequent imaging examinations. This saves radiologists a significant amount of time while ensuring accurate and efficient patient care. Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods in the last years. Despite their advancements, these methods have not yet realized their full potential.

The aim of this PhD project is to develop a cutting-edge deep-learning-based image registration framework. This framework will be able to seamlessly integrate clinical insights during network training, tailoring its performance to specific medical applications. Your primary focus will be advancing image registration techniques for lesion tracking in cancer patients between images taken at several stages of the treatment. Moreover, your ingenuity and interests will guide the exploration of diverse application areas within the medical imaging landscape.

As a PhD candidate you have the following tasks and responsibilities:

• Conduct research in the development of image registration methods.
• Collaborate with a multidisciplinary team to translate cutting-edge technology into clinical practice.
• Publish research findings in peer-reviewed journals and present at international conferences.
• Mentor junior team members and contribute to the development of the lab's research direction.
• Co-organize the international Learn2Reg image registration challenge and be active in the research community.

You should be a creative and enthusiastic researcher with an MSc degree in Computer Science, Mathematics, Physics, Engineering or similar. Moreover you have:

• A clear interest to develop artificial intelligence algorithms and an affinity with healthcare.
• Good communication skills and enjoy working in a multidisciplinary team.
• Expertise in software development, preferably in Python.
• Experience with deep learning, machine learning, and image analysis or time series analysis is a plus.

Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.

• A gross monthly salary between € 2.789 and € 3.536 (scale 10A) based on full-time employment.
• From 1 November 2023 the wages will increase by 4%.
• An annual vacation allowance of 8% and an end-of-year bonus of 8.3%.
• If you work irregular hours, you will receive an allowance.
• As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year.
• Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
• Discount on your supplementary health insurance. You can make use of two collective health insurance policies: UMC Health Insurance and CZ Collective. They offer many extras to stay vital and healthy, such as a prevention budget, workshops, and apps.

In addition to our terms of employment, we also offer employees various other attractive facilities, such as childcare and sports facilities. Want to learn more? Take a look at the Cao UMC.

You will work in the Diagnostic Image Analysis Group (DIAG). DIAG is part of the Departments of Imaging, Pathology, Radiation Oncology, Cardiology, and Neurology of Radboud University Medical Center.
We develop computer algorithms to interpret and process medical images. The group currently consists of around 70 researchers. Radboud University Medical Center and Radboud University are located in Nijmegen, the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands. Radboud University has over 17,000 students. Radboud UMC is a leading academic center for medical science, education and health care with over 8,500 staff and 3,000 students.

Radboudumc
Welcome to Radboud university medical center (Radboudumc), where scientific breakthroughs are born through the curiosity and passion of our collaborating researchers in a vibrant environment. We believe trust and excitement are crucial elements to achieving our goals, and we approach our research as teams and with the utmost rigor to make a significant impact on health and healthcare. Our researchers are driven by their fascination with the biological, psychological, and sociological mechanisms underlying health and healthcare. They collaborate with partners from all over the world to improve health outcomes for all. Radboudumc unites patient care, research, education, and corporate learning, which allows us to approach our mission to shape the health and healthcare of the future in an innovative and person-centered way.

Our ambition is to lead the way in the pursuit of prevention, sustainability, meaningful care. We believe that through our research, we can significantly improve the health and well-being of society. Join us on our mission to make a difference in healthcare. Become a part of our community of gifted researchers, professionals or patient partners who are dedicated to making a real impact on population health and healthcare.

Read what it is like to do a PhD at the Radboud University Medical Center.

Read more about our strategy and what working at Radboud university medical center means. Our colleagues would be happy to tell you about it. #weareradboudumc

Any questions? Please contact Alessa Hering. Use the Apply button to submit your application.