Medical image analysis, inverse problems in medical imaging and machine learning are becoming more and more intertwined in theory as well as in applications. We are looking for two postdoctoral candidates to work on the development of new machine learning methods at the interface of mathematical imaging, inverse problems, and medical image analysis. Potential research topics include learned image reconstruction, geometric deep learning, and physics-informed neural networks for 4D imaging.
The research will be conducted in the [Applied Analysis](https://www.utwente.nl/en/eemcs/sacs/) group of the Department of Applied Mathematics of the University of Twente. We are a growing research group at the intersection of inverse problems, geometric deep learning, and physics-informed machine learning with applications in medical imaging. We closely work with collaborators in hospitals and industry and are strongly involved in activities around AI in our university's Technical Medical Center [(Techmed)](https://www.utwente.nl/en/techmed/) and Digital Society Institute [(DSI)](https://www.utwente.nl/en/digital-society/)
This position is part of a national 4TU program on [Precision Medicine](https://www.4tuprecisionmedicine.net/) that includes four technical universities and several academic hospitals. As such, you are expected to collaborate with physicians and physicists on the combination of model-driven and data-driven approaches for image formation and analysis in a range of imaging modalities, including MRI, CT, and ultrasound.
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