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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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understanding on the origin of nucleic acids that are shed in liquid biopsies, such as blood, using cancer models (mouse and rat) and patient samples of neuroblastoma disease, a rare childhood cancer. Nucleic
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drive neuroinflammation in synucleinopathies and other neurodegenerative diseases. The candidate will employ advanced in vivo models and spatial technologies to dissect the roles of tissue-resident
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candidate will work closely with experts in food allergy, neuroimmunology, gut physiology, and computational biology to characterize immune cell responses, construct spatial maps of inflammation along the gut
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this disease and reveal the mechanisms underlying the strong cell-type specific effect of the CMT-causing YARS mutations. For this we will generate high-resolution spatial chromatin organisation, chromatin