Medical Image Analysis group
The Medical Image Analysis Group, led by Prof. Josien Pluim, aims to create solutions that help support diagnosis, prognosis and image-guided treatment in clinical care. The group's research includes applied as well as methodological innovations to advance analysis of medical images. The research has a strong focus on machine and deep learning. The group is internationally competitive in the field of medical imaging.
The current faculty of the group cover a range of research areas, including image analysis and quantification for cardiology, neurology and oncology. More specifically, the group is known for its work in the field of computational pathology. Another a strong line is on MR image analysis and RF safety through collaborations with Philips and with the University Medical Center Utrecht. A third key focus is on AI-based image guidance of therapies (radiotherapy with the novel MR-linac system, robot-assisted surgery).
Continued advancement of methodology is crucial and all faculty work on methodological innovation in parallel to their applied focus. Examples include deep learning methods for image synthesis and deformable image registration.
The group is involved in EAISI, TU/e's cross-departmental institute for Artificial Intelligence, and in several national and international AI consortia. Research is performed in direct collaboration with clinical partners. Furthermore, industrial partners closely participate in a large percentage of the projects.
The educational curriculum of the group concerns machine learning and medical imaging at the bachelor and master level for students of the Department of Biomedical Engineering. Moreover, a joint master track in medical imaging was initiated together with the Center for Image Sciences at the University Medical Center Utrecht.
Collaborations and opportunities
The medical imaging field in the Netherlands is extensive and internationally leading. The Medical Image Analysis group at TU/e is positioned well within the Dutch landscape, with good connections to the other groups. We are a partner in large national research consortia, but also participate in various international networks.
In the Department of Biomedical Engineering, imaging and AI are topics of common interest across research groups, as for instance in ultrasound imaging and systems biology. Within TU/e, collaborations exist with the Department of Electrical Engineering, on for example neuroimaging, and with the Department of Mathematics and Computer Science, on methodological advancements of AI.
You have proven expertise in machine/deep learning and preferably a strong track record in medical imaging. Faculty in the department is expected and supported to have their own research line, independent of but closely related to those of other faculty. The vacancy is positioned within the Medical Image Analysis group, but the position may evolve into an independent one in future.
Your research line complements the current strengths of the group and the department. To give you an impression, your line could be focused on methodologically oriented innovation in AI. Some of the challenges that currently hamper application of AI in routine clinical care include explainable AI, analysis of input data from very different sources (e.g. the combination of imaging, genetic information and patient metadata) and the integration of prior (model) knowledge. A more application-oriented AI line of research, on the other hand, could cover analysis of MR images or image analysis for oncology, pediatrics or any of the collaborative topics within BmE/TUe. Note that the list is not exhaustive and other research profiles will be considered.
You have an affinity with teaching and enjoy training the young generation of professionals. Around 30 enthusiastic master students join the group each year, performing research projects, often with our clinical/industrial partners. Many of these projects result in publications.
Questions regarding the academic contents of the position can be directed to
prof. Josien Pluim, j.pluim[at]tue.nl.
For information about terms of employment, please click here or contact Sascha Sanchez van Oort, HR Advisor, s.j.m.g.sanchez.van.oort[at]tue.nl or +31 40 247 7310.
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