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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 following fields: signal and image processing; wireless networks and ad hoc networks ; computational intelligence methods in technology, industry and medicine; human-computer interface design and development
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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
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spectroscopy (e.g. CD, FTIR, NIR), fluorescence techniques (e.g. fluorescence anisotropy, time-resolved fluorescence, fluorescence lifetime imaging), HPLC chromatography and the application of machine learning
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the necessary interface between the computer and the neural network of the human retina in vivo – without introducing additional modifications to our organisms. Such an interface could, in turn, be developed