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Jhunjhunwala Lab at Genentech, please go to: https://www.gene.com/scientists/our-scientists/suchit-jhunjhunwala Relevant publications: Thrift, W. J. et al. Graph-pMHC: graph neural network approach to MHC class
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. The successful applicant will have an opportunity to work with multiple groups with expertise spanning scientific disciplines and approaches, including oncology, single-cell biology, spatial transcriptomics, high
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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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the molecular pathways of disease and to establish the mechanism of action of our therapeutics. The successful candidate will work with our team to analyze multi-level biomarker data generated from pre-clinical
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discovery. This position in particular focuses on sequence-to-function deep genomics modeling, with the goal of developing performant models that make generalizable out-of-distribution predictions
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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
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and interdisciplinary group with diverse areas and commitment to tackle challenging problems in biology and medicine and will work on independent research projects, which leveraging collaborations
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characterization of membrane protein complexes that mediate inflammatory signaling by using a confluence of structural, biochemical, cell biological and computational tools. The candidate is expected to work
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computational, the project is highly collaborative, involving regular interaction with laboratory colleagues in the Discovery Oncology therapeutic area. The Opportunity: Opportunity to work closely with
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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