Every day more than 800 women die as a consequence of their pregnancy, of which the vast majority occur in low-income countries. Ultrasound imaging can be used to detect maternal risk factors and is recommended by the World Health Organization. Unfortunately, ultrasound is barely used in these countries. This is caused by the lack of trained sonographers that can perform prenatal scanning and the high cost of ultrasound equipment.

The latter obstacle is alleviated by handheld ultrasound devices that are now becoming available on the market for relatively low prices. Most of these devices can be connected to a smartphone or tablet. To obviate the need of a trained sonographer, we propose to combine a standardize acquisition protocol (consisting of several sweeps over the belly of the pregnant woman) with real-time feedback by deep learning algorithms followed by automated interpretation with deep learning. In the past, algorithms were developed in our group for automated to detect twin pregnancies, estimate gestational age, determine fetal presentation and determine placenta location. All developed algorithms were ported to an Android based app called the BabyChecker and our industry partner Delft Imaging has started data collection and field tests in several African countries. Delft Imaging is funding this Ph.D. position.

Tasks and responsibilities
You will work on extending the capabilities of the BabyChecker with:

• Improving the real-time feedback to guide during the acquisition of the sweeps, so that acquisition errors can be prevented or immediately corrected.
• Developing and validating several new automated algorithms for example for the detection of fetal viability, detection of placenta previa, and estimation of the amount of amniotic fluid.
• Improve the performance of the existing algorithms.
• Increase the robustness of the deep learning algorithms so they provide accurate results with data from multiple types of hand-held ultrasound devices that are available on the market today.

All algorithms should be computationally efficient so they can be run on a smartphone. We expect you'll be using and extending state-of-the-art frameworks for deep learning on low-cost devices. We also expect you to be active in data collection and field testing and interested to travel to Africa and other sites where the BabyChecker is used.

You should be a highly motivated, creative, and enthusiastic researcher with an MSc degree in Computer Science, Data Science, Physics, Mathematics, Engineering, or related. You have a good understanding of deep learning and have the skills to adapt deep learning algorithms. Good knowledge of medical imaging, ultrasound in particular, is a plus. You need excellent communication skills. The research should result in a Ph.D. thesis.

Fixed-term contract: 4 years.

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.

Radboudumc

You will be part of two research groups within the Department of Medical Imaging at Radboudumc: the Medical UltraSound Imaging Center (MUSIC) and the Diagnostic Image Analysis Group (DIAG).

MUSIC is an international expertise center that develops ultrasound based techniques for improved diagnosis, guiding intervention and monitoring treatment. The close collaboration with clinicians on one side and commercial partners on the other side warrants that these techniques not only result in academic output, but also are translated to clinical applications and when appropriate to commercial products. Point-of-care ultrasound (ultrasound using handheld devices) is a new research line within MUSIC.

DIAG is an internationally renowned group on deep learning for medical imaging. DIAG currently has 50 deep learning researchers focused on various medical image analysis topics. The focus of DIAG is the development and validation of novel methods in a broad range of medical imaging applications. The key to the success of DIAG is close cooperation with clinicians. A team of scientific programmers supports deep learning research, maintaining a high-performance compute cluster with over 100 GPUs for large-scale experimentation.

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