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Field
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developed mouse models and human tissue processing pipelines. The candidate will learn and use cutting edge immunological methods (high parameter flow cytometry, multiplex imaging, single cell and spatial
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple
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process in imaging optics is inherently multi-objective, involving different competing performance criteria. This could be on a more systematic level the interplay of quality, cost and manufacturability but
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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of Amsterdam, under the supervision of Prof. Noushine Shahidzadeh (expert on crystallization and sol-gel process in confinement) and Prof. Hannelore Derluyn (expert in micro-CT 3D imaging for porous media
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knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux systems and High Performance Computing Good oral and written presentation skills in Norwegian
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reconstruction, processing and quality-control tools in a transparent and reproducible way. You will have the opportunity to: Develop a modern, open and extensible software ecosystem for optoacoustic image
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18 Sep 2026 Job Information Organisation/Company CNRS Department Grenoble Images Parole Signal Automatique Research Field Physics Researcher Profile First Stage Researcher (R1) Application Deadline