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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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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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streamline and accelerate the development of the projects. Share research through scientific publications, national and international conferences, and internal presentations. Who You Are: PhD graduate in
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applicant will have an opportunity to work closely with a diverse scientific team that includes microbiologists, immunologists, cell biologists and bioinformaticians. Who You Are: Recent PhD in the field
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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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) 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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for analyzing complex mass spectrometric data. Foundational biochemistry skills and an essential understanding of tumor-related biology. Strong interpersonal skills to build effective working relationships across
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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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data to join the Departments of Proteomics and Genomics Technologies and Neuroscience at Genentech, Inc. We have in-house cutting-edge technologies including proteomics, lipidomics, NGS, CRISPR screens
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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