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computational scientists. The Opportunity: Develop and apply advanced in vitro and in vivo models to investigate tumor suppressor function and cancer signaling pathways. Leverage cutting-edge technologies
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up mechanistic investigations of novel findings. You will work at the interface between disciplines and leverage expertise from different research areas including proteomics, functional genomics
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clinico-genomic/response data, design training over baseline, on-treatment and post-treatment time points, and model treatment effects with both mechanistic and data-driven components. You will work with
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laboratory colleagues in the Discovery Oncology therapeutic area. The Opportunity: Opportunity to work closely with computational colleagues to build, train, and evaluate cutting edge AI models using large
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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a highly collaborative and interdisciplinary group with diverse areas and commitment to tackle challenging problems in biology and medicine and will work on independent research projects, which
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epigenetic regulation of fibroblast responses to inflammatory stimuli in the context of chronic lung diseases. Additionally, your work will address key project team questions related to both forward and
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using pluripotent cells is highly desired. Team player with a proactive attitude to tackle long standing, difficult problems in retinal biology. Highly motivated and capable of independent work in a
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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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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