Patient-level data exchange has an increasingly challenging future in the health sector due to privacy concerns. An alternative for sending data across institutions is to keep data at its original institutional source and access it only through a federative approach. We believe all technical capabilities are available on an adequate level, but that the next step towards innovation is lacking. This is because (a) it is unclear how to put all pieces together, and (b) certain obstacles in practice have been overlooked until now. The University of Twente with its unique entrepreneurial spirit in engineering aims to address this by collaborating with the Dutch Institute for Clinical Auditing (DICA), and the Netherlands Comprehensive Cancer Organisation (IKNL), to re-engineer their registries for breast implants (DBIR) and breast cancer (Netherlands Cancer registry / NABON Breast Cancer Audit) according to the federative analytics approach as a showcase for the health sector in The Netherlands and beyond. This position offers the unique opportunity to contribute to the field of federated learning, help ensure true uptake of this technology in society, and to collaborate with world-renowned experts in the field.

Key Responsibilities:

• Design and implement a proof-of-concept showcase implementation of an analytical product relevant to DICA and/or IKNL that uses a federative approach for deriving the aggregated results.
• Inter-disciplinary collaboration with various research institutes, hospitals, and companies to arrange this multi-institution showcase.
• Publish and present research findings and experiences in leading scientific and professional venues explaining the consequences and benefits of federated learning.
• Staying updated with the latest advancements in federated learning both in research as well as industry.
• Contribute to the mentoring of graduate students and junior team members.

The Postdoctoral Researcher will be embedded in the DMB research group and closely collaborate with the HTSR research group. The supervision lies with Prof.dr.ir. Maurice van Keulen (DMB) and Dr. Marissa van Maaren (HTSR and IKNL). This position is part of the “Federated Analytics through Harmonization in National Breast Cancer Care” project.

The ideal candidate we are looking for has:

• A PhD degree in data science, computer science, or a related field with affinity for health care, or
• PhD degree in (technical) medicine, biomedical engineering, or a related field with an affinity for computer science.
• Broad and application-oriented knowledge and interests in federated learning, secure computing, statistics, and predictive modelling.
• Comfortable with programming and the command-line to meet technical requirements and at the same time confident in interactions with the medical field to understand the clinical needs.
• An enthusiastic attitude towards learning new things and staying updated with the latest advancements.
• A committed team spirit and the ability to work in an interdisciplinary environment.
• Strong communication and social skills.
• Excellent command of English; Knowledge of Dutch is required on a sufficient level for effective oral and written communication with Dutch native speakers.

• You will be appointed for a period of maximum 1 years full-time within a very stimulating scientific environment. The university offers a dynamic ecosystem with enthusiastic colleagues.,
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU);
• Gross salary between € 3.557,- and €4.670,- per month depending on experience and qualifications;
• Excellent benefits including a holiday allowance of 8% of the gross annual salary, a year-end bonus of 8.3% and a solid pension scheme;
• The flexibility to work (partially) from home;
• Free access to sports facilities on campus
• A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours. A full-time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis.
• Excellent support for research and facilities for professional and personal development.
• We encourage a high degree of responsibility and independence, while collaborating with close colleagues, researchers and other university staff.
• We are also a family-friendly institution that offers parental leave (both paid and unpaid) and career support for partners.

The DMB research group is a diverse interdisciplinary group of researchers at the forefront of advancements in data engineering, machine learning, deep learning, and computer vision to advance scientific knowledge and societal welfare in a large spectrum of data science applications.