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applications. As a Postdoc candidate at TU Delft, working in close collaboration with a PhD student and our industrial partner Delmic and academic partner AMOLF, you will develop a proof-of-concept prototype
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fibrosis. Together with the department of surgery, focusing on vascular surgery, we aim to to ulitse high-throughput biomaterial screening to identify the optimal physicochemical surface properties
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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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affects the performance of plasmonic structures in photovoltaics and imaging. About this position In this position you will leverage materials with high thermal conductivities (e.g. 2D materials) to achieve
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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
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Radboud University Medical Center (Radboudumc); 10 Oct ’25 published | Netherlands | about 1 month ago
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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cell lines have been engineered and characterised, you will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models
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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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screening radiologists and industry to develop new and improve existing methods and processes in screening breast imaging. Our direct connection with the Dutch Expert Centre for Screening (LRCB ) ensures and
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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In