183 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Postdoctoral positions at Nature Careers
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) Limited until: 31.05.2027 Reference no.: 4634 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re
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, social-emotional and behavioral. The successful applicant will be affiliated with the Institute for Teaching and Learning situated within the Department of Education and Social Work (DESW). The
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strong background in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python
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for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field strong background in image processing and analysis, including deep learning
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cutting-edge technologies — from single-cell multi-omics and deep learning to live-cell imaging and stem-cell–based organoid systems — to predict, observe, and manipulate epigenetic processes. Our lab and
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, bright individuals with a diploma or master’s degree in bioinformatics, biochemistry, biology, molecular/translational medicine or related subjects and a PhD in life and computer sciences. Previous
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assays, molecular biology, and rodent biology is required without exception. The ability to learn and adopt new techniques, particularly bioinformatics, immunohistochemistry and flow cytometry, is
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presentations at international conferences Willingness to learn and interest in interdisciplinary applied research We offer: A varied and challenging job within a top-class network from politics, science
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' to develop neural networks with remarkable information content: flies, which we use as a model, have brains that compute flying in 3D, navigation, metabolism and advanced learning and memory capabilities - all
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into neuroendocrine output to instruct adaptive physiology and behavior. Conversely, we seek to understand how internal states shape our physiological responses to the same cues. Our research is based on the premise