The Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, Nijmegen, The Netherlands, is seeking a research software engineer to join our growing team. This is an excellent opportunity to have an impact on the field by translating our state-of-the-art deep learning research in pathology into robust software that could be integrated into the pathologists workflow.
We offer a challenging research environment in a growing international research group with ample opportunities to further develop your skills.
In this position, you will be responsible for leading the development and managing the full lifecycle of our pathology workstation. This includes understanding user needs, creating requirements, developing the workstation, integrating algorithms, automating tests, writing documentation, releasing, supporting users, maintenance and publication in peer-reviewed journals.
You will work in the RSE team of DIAG, and work in close collaboration with deep learning algorithm developers and end users. You will be supervised by James Meakin, Francesco Ciompi and Geert Litjens.
You should have a MSc or PhD degree in Computer Science, Physics, Biomedical Engineering or another technical field or equivalent practical experience.
We are looking for a self-motivated research software engineer with an interest in medical imaging and excellent communication skills in English. You should have experience with developing software that is used by others, any experience developing scientific software or open source software is a plus. You should have demonstrable experience of building software in Python. You should be familiar with web technologies, and it would be advantageous if you have developed a server-side application or have experience with Django, Qt, MeVisLab, WebSockets, Ansible and/or Docker.
Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG) and there will be, depending on the type of job, a screening based on the provided cv. Radboud university medical center's HR Department will apply for this certificate on your behalf.
The Computational Pathology Group is a research group of the department of Pathology of the Radboud University Medical Center (Radboudumc). We are also part of the cross-departmental Diagnostic Image Analysis Group (DIAG) at Radboudumc, with researchers in the departments of Radiology and Nuclear Medicine, Pathology and Ophthalmology. We develop, validate and deploy novel medical image analysis methods, usually based on machine learning technology and focusing on computer-aided diagnosis (CAD). Application areas include diagnostics and prognostics of breast, prostate, lung and colon cancer. For this purpose, we are developing an efficient workstation for viewing and annotating pathology slides in close collaboration with Fraunhofer MEVIS, Germany. The prototype of this workstation is a server-side web application that has been developed on MeVisLab.
The Research Software Engineering team is a group within DIAG who also have expertise in software engineering and specialize in research that is driven by robust and reproducible research software. This software is made open source where appropriate (https://github.com/DIAGNijmegen) and published in academic journals. We also provide other researchers who do not have a background in software engineering education on the fundamentals, and promote best practices in software development learnt from industry. We also aim to have a direct impact on healthcare by translating some of the group's research software output into the clinical workflow for our in-house radiologists, pathologists, and ophthalmologists.
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.
Read more about what it means to work at Radboudumc and how you can do your part.