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HCI and cybersecurity, to cancer research tools and methods for numerical analysis and machine learning. The research work takes place in a multidisciplinary team with a focus on image processing with
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within the University of Münster developing imaging methods allowing to visualize molecular processes inside organisms, tissues and cells. With the help of imaging, we perform cutting-edge research in
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researcher will primarily work on developing methods for the analysis and interpretation of large datasets, where data are generated in the form of microscopy images and electrical signals from experimental
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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interactive environment. Facilities include Protein Expertise Platform, X-ray, proteomics, NMR (850-400 MHz), cryo-EM and Biochemical Imaging Centre (confocal, SIM, FLIM, spinning disk, TIRF, STORM). Learn more
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computer vision, where the PhD project was fully or substantially method-focused on computer vision and/or AI-based image or video analysis have very strong knowledge of machine learning, with practical
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sequencing or spatial transcriptomics. Candidates without substantial practical experience in stereotaxic surgery combined with neural recording or imaging will not be competitive for this position. Duties
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to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical, biological, and methodological
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and molecular genetics as well as hands-on experience with cloning, live-cell fluorescence microscopy, image analysis, and sample preparation for sequencing and multi-omics analyses. The main model