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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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spatial transcriptomics, genomics, or imaging-based methods. Experience with image analysis, single-cell or spatial omics data analysis. Familiarity with machine learning frameworks (e.g. PyTorch
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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integrate genetics, cell biology, genomics, and bio-computing to unravel plant biological processes and to further translate this knowledge into value for society. Please visit us at www.psb.ugent.be for
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at the heart of a renowned Plant Biotech campus in Ghent, Belgium. Its mission is to integrate genetics, cell biology, genomics, and bio-computing to unravel plant biological processes and to further translate
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at the cellular level, and (iii) applying quantitative image analysis to compare structural organization across fertile and infertile donors. The project is embedded in an active collaboration with a local
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is to integrate genetics, cell biology, genomics, and bio-computing to unravel plant biological processes and to further translate this knowledge into value for society. Please visit us at
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processing of the omics datasets will guide selection of differentially regulated key genes to be evaluated for their therapeutic potential in our YARS1 Drosophila and iPSC models. Guided by the unmatched