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and English will be an additional advantage; especially in the areas of: biocybernetics, deep learning, image analysis, image navigation, programming and computer graphics 6) completed a foreign or
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activity (TON, TOF) and selectivity will be used to develop predictive machine-learning models that enable accurate in silico identification of optimal catalyst–substrate–conditions combinations and the
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data integration data quality data analytics programming languages (SQL, Python, Java) Complementary IT skills: machine learning image recognition sensor technologies robotic technologies
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or information and communication technology, 2) experience in teaching, 3) experience in machine learning, deep learning, particularly in the application of biomedical data processing, 4) experience in processing
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on large-scale sequencing data or murine models. Primary cells will be incorporated into a commercial ToC model (idenTx3), stained with apoptosis and cell death dyes, and imaged using a live-imaging
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substrate–catalyst–conditions combinations). The resulting experimental dataset on catalyst activity (TON, TOF) and selectivity will be used to develop predictive machine-learning models that enable accurate
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g