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postdoc position (with a possible extension for 1 more year) to lead cutting-edge research in remote sensing and deep learning as part of the Ethio-Nature project, a major international research
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and analysis, human-machine interaction, productivity monitoring, and proactive personalized feedback and learning methods (using augmented and/or virtual realities). We seek excellent candidates with
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ATAC-seq, single-cell RNA-seq, spatial gene expression, and whole-genome sequencing (with long reads) data. The candidate will get the opportunity to explore new analysis methods using deep learning
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Learning (DL) tools tailored specifically to the particularities of TCR interactions. As part of the Deep Immune Receptor Modeling (DIRM) grant from the NNF Data Science Collaborative Research Programme
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epitopes and c) lack of Deep Learning (DL) tools tailored specifically to the particularities of TCR interactions. As part of the Deep Immune Receptor Modeling (DIRM) grant from the NNF Data Science