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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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, spatial data, and perturbation screens with high content molecular and imaging data to understand cellular and multi cellular combinatorial programs in cells and tissues in health and disease. You will join
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) organization which uses data, by developing best in class computational methods, and applying them to the most relevant scientific problems across all stages of the pipeline. This position is based within
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field; or a Ph.D. in (Molecular) Biology or Immunology with in-depth experience in high-throughput data analysis evidenced by publications. ● You have experience with single-cell and multi-omic data
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challenges, including data analysis, hypothesis generation, and experimental design optimization. We seek a highly motivated postdoctoral candidate with expertise in AI, machine learning, and computational
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structural biology to tackle challenging scientific questions. Your responsibilities will include, but are not limited to: Multi-omics analysis of bulk and single-cell sequencing data. Developing deep learning
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collaborative and multidisciplinary environment. For information about the Lamba Lab at Genentech, please go to: https://www.gene.com/scientists/our-scientists/deepak-a-lamba Postdoc Program Elevate your research
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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
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) organization which uses data, by developing best in class computational methods, and applying them to the most relevant scientific problems across all stages of the pipeline. This position is based within
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teams. The Opportunity: Opportunity to work closely with computational colleagues to analyze, evaluate, and perform integrative computational analyses. Leverage multimodal high dimensional data to explore