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Field
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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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of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment
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anesthesiology, diagnostic imaging, animal nursing, epidemiology, laboratory animal medicine, surgery, clinical pathology, medicine, and domestic animal reproduction. In addition, the SLU University Animal
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own way, shaped by the sensory systems it uses to detect and interpret the environment. Nikolaev lab investigates how vertebrates sense and process environmental signals across scales, from molecules
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to fluorescently labelled AP-L complexes and analyze how the cells accumulate and spread the complexes over time using live cell imaging, immunocytochemistry, ELISA, Western blot, electron microscopy and other
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practical skills in the field of data science, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python
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: developing and applying image analysis and sensor technologies (e.g. RGB/NIR) for textile production, as well as using machine learning for process optimisation and performance prediction from fibre
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data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems
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, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python) are required. Ability to communicate effectively in both
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-driven life science framework. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular