PhD candidate 'AI-driven tumor detection in combined MRI-Rad...
Would you like to apply your technical skills to cure cancer? Come join our research lab at the radiotherapy department!
In radiation therapy a device called the linear accelerator (linac) shoots beams of high energy photons at a tumor, killing the malignant cells. Unfortunately, this can damage nearby healthy tissue as well. Therefore, it is crucial to obtain the highest level of accuracy in radiation dose delivery. This is difficult however, when tumors are located in or near organs that move a lot (breathing, heartbeat, etc). But there is a solution: The Radboudumc is one of a few places in the world that house an MR-Linac, an integrated system of a linac and an MRI scanner. Using MRI we can rapidly make images of the moving tumor and its surroundings and adjust the photon beam accordingly. This is where you come in.
We are looking for a PhD candidate to develop Artificial Intelligence tools to automatically detect healthy organs and tumors in the MR images. At present this is done manually at the start of a treatment session. This takes such a long time that it prevents effective (real-time) beam adaptation. Your role will be to create deep-learning networks that can delineate the relevant structures in the image in a flash.
Secondly, you will create MRI protocols that allow faster scanning and with improved tissue/tumor contrasts to facilitate aforementioned delineation and also treatment efficacy evaluation. You will closely work together with a second PhD student on this project, who will develop tools that use the delineation results to steer the linac’s beams more efficiently and effectively. Ultimately, the goal is to accelerate the entire workflow to such an extent that real-time beam adjustments can be achieved. 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.
In addition to your monthly salary and an annual vacation allowance of 8%, you will receive 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.
You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
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. Our group operates at the interface of the Radiation Oncology department and the Diagnostic Image Analysis Group (DIAG) which is housed in the Radiology department. The Radiation Oncology department is a clinical department where patients are treated using a variety of radiotherapy delivery techniques including image-guided adaptive radiotherapy by the MR-Linac, and where research takes place varying from investigations on the molecular structure of tumor and immune cells to improving the daily treatment routine. Due to the very technologically advanced nature of the treatment modality, the department has a very large clinical physics team to both develop methods for, and maintain the high-tech radiation therapy devices.
The DIAG is a very large research group heavily focused on developing AI methods for medical image analysis over a wide range of imaging modalities. It runs the world-leading medical AI challenges platform which brings together AI researchers, medical specialists and medical technology industry.
Research Institutes At the moment there are more than 1,300 PhD candidates at our medical hospital. This number includes PhD candidates on our pay roll as well as external candidates (those employed somewhere else but researching on our premises).
Radboud Institute for Health Sciences: ± 700
Radboud Institute for Molecular Life Sciences: ± 400
Donders Center for Medical Neurosciences: ± 200
Read what it is like to do a PhD at the Radboud University Medical Center.
Radboudumc Radboud university medical center is a university medical center for patient care, scientific research, and education in Nijmegen. Radboud university medical center strives to be at the forefront of shaping the healthcare of the future. We do this in a person-centered and innovative way, and in close collaboration with our network. We want to have a significant impact on healthcare. We want to improve with each passing day, continuously working towards better healthcare, research, and education. And gaining a better understanding of how diseases arise and how we can prevent, treat, and cure them, day in and day out. This way, every patient always receives the best healthcare, now and in the future. Because that is why we do what we do.
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 You have completed an MSc in Biomedical engineering, Computer science, A.I., Medical imaging, Physics or similar. You are enthusiastic and motivated to successfully complete a PhD. You have well-developed communicational and social skills directed towards working in a team.
You have experience in medical imaging, image processing, and AI methods.
You have programming experience (Python is a plus).
Excellent communication skills in English, both written and spoken.
Ability to work effectively and pro-actively, both independently and as part of a multidisciplinary team.
All additional information about the vacancy can be obtained from Dr. Ir. Peter Koopmans, Assistant Professor MR-guided Radiotherapy.
Use the Apply button to submit your application.