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Field
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image processing and analysis method development. The position builds on the lab's track-record in the field of computational imaging techniques for super-resolution microscopy and image analysis
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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation
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of the following areas: Image processing, partialdifferential equations, scientific computing, deep learning.•Solid programing skills. Knowledge of Python, Tensorflow/Pytorch, and Mathematica.•Fluent
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of image sensor technology through advanced pixel design Your key responsibilities include: Researching novel application-specific pixel architectures Developing hardware for characterization and application
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Image Analysis Group (DIAG) at Radboudumc. We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided
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mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every device and product we use nowadays, we
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combined with the ability to work efficiently both independently and as part of a team. Moreover, you have: a PhD in (bio)medical sciences/molecular imaging; research experience in molecular imaging
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intelligence-based automated interpretation of medical images, and new knowledge on the human visual system into the screening mammography reading process. Combining these new capabilities and new knowledge has
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mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every device and product we use nowadays, we
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aims to study how the brain processes pictures and sequences of pictures, like in comics. These PhD positions will study how sequences of images combine images together into hierarchic relationships