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immunotherapy with graph neural networks trained on spatial single-cell tumor microenvironment (TME) data from non-small cell lung cancer (NSCLC). Using high-dimensional datasets, you will learn bi-directional
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—including next-generation sequencing, single-cell approaches, genome engineering, and spatial biology—to study mechanisms of tumor initiation and progression. Collaborate across disciplines to translate basic
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analysis. Experience with spatial assays and/or clinical data is preferred but not required. ● You have experience with in vivo or in vitro models, preferentially relevant to lung biology. ● Excellent
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hypotheses. This is a fantastic opportunity to contribute to world-class science in a leading biotechnology company. Who you are: Candidates must have a PhD in Computational Biology or Computational Science
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laboratory experiments Working knowledge of biochemical techniques is a plus. For information about the Vucic lab at Genentech, please go to: https://www.gene.com/scientists/our-scientists/domagoj-vucic https
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teams. To learn more about the Lill Lab, please visit: https://www.gene.com/scientists/our-scientists/jennie-lill To learn more about the Dey Lab, please visit https://www.gene.com/scientists/our
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collaborative and multidisciplinary environment. Previous experience with proteomics is not required. To learn more about the Ori Lab, please see link below: https://www.gene.com/scientists/our-scientists
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synthetic chemistry and reaction optimization. A working knowledge of molecular biology and/or protein generation. For information about the (lab) at Genentech and publications, please go to: https