The Research Software Engineering team is a group of professional software engineers within DIAG, who specialize in the development of robust and reproducible academic software. Our aims are firstly to support researchers in their algorithm development by providing education on software engineering fundamentals, to promote best practices learnt from industry, and to provide stable software and systems that they can use in their research. Secondly, we aim to have a direct impact on healthcare by translating these algorithms into the clinical workflow, developing medical products that can be used by our in house radiologists, pathologists and ophthalmologists.

In this position, you will expand the previously developed software by adding functionality, in close collaboration with end users and deep learning researchers. The software will be used in ongoing research projects and in a clinical setting. You will work within the RSE team of DIAG. You will be supervised by James Meakin and Colin Jacobs.

As a research software engineer, you should have an BSc, MSc or PhD degree in Computer Science, Physics, Engineering or Biomedical Sciences or similar. For BSc candidates, more than 3 years of experience in software development is required.

We are looking for a highly motivated and talented software developer with

• Experience in developing (scientific) software;
• Experience in Python and C++;
• Strong interest in medical image analysis;
• Excellent communication skills;
• Experience with deep learning would be an advantage;
• Experience with Qt would be an advantage;
• Experience with MeVisLab would be an advantage.

You recognise yourself in the Radboud way of working.

Fixed-term contract: 1 year with a possible extension.

Scale 10: max € 58838 gross per year at full employment (incl. vacation bonus and end of year payments).

We offer a challenging research environment in a growing international research group with ample opportunities to further develop your skills. Employment will be initially for one year, with an intention to extend to at least 2 years.

Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG). Radboud university medical center’s HR Department will apply for this certificate on your behalf.

Read more about the Radboudumc employment conditions

Radboudumc (university medical center)

Radboudumc strives to be a leading developer of sustainable, innovative and affordable healthcare to improve the health and wellbeing of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare. To get a better picture of what this entails, check out our strategy film.

Our key strength is medical life-sciences and clinical practice, with an impressive infrastructure comprising state-of-the-art technology platforms and (translational) research facilities. The Radboudumc is therefore uniquely positioned in the emerging Euregio and Dutch healthcare infrastructure to play a leading role in the new healthcare paradigm of prediction, prevention and personalised medicine.

The Radboudumc focuses on scientific health challenges of today, with an eye on emerging diseases of the future.

Read more about what it means to work at Radboudumc and how you can do your part.

https://www.radboudumc.nl/EN/Pages/default.aspx

The Diagnostic Image Analysis Group (DIAG) is a research group of the Department of Radiology and Nuclear Medicine of the Radboud University Medical Center. Nijmegen is 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. Radboudumc is a leading academic center for medical science, education and health care with over 8,500 staff and 3,000 students. The focus of the Diagnostic Image Analysis group is the development and validation of novel methods in a broad range of medical imaging applications. Research topics include image analysis, image segmentation, machine learning, and the design of decision support systems. Application areas include neuro, breast, prostate, lung and retina imaging and digital pathology. Key to the success of the group is close cooperation with clinicians. Currently the group consists of around 40 researchers.

Our group has a very strong track record on developing and evaluating automatic algorithms for early detection of lung cancer. We have organized the highly successful ANODE09 and LUNA16 challenges, and contributed to the one million dollar Kaggle Data Science Bowl 2017. These challenges all received great interest from the medical image analysis and data science community. Based on our own experience and the challenges we organized, we have developed state-of-the-art deep learning algorithms for lung nodule detection and characterization, which we are still improving and continuously evaluating in clinical observer studies. This project has resulted in the release of Veolity, an efficient workstation for reading lung CT scans, which has been developed in close collaboration with MeVis Medical Solutions AG, Bremen, Germany.