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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project in collaboration
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on advanced machine learning applications in computational pathology, medical imaging, and clinical text analysis. They will be expected to develop deep learning models for analyzing whole-slide histopathology
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deep learning techniques for analyzing high-dimensional omics data, identifying predictive biomarkers, and understanding cancer heterogeneity. Projects include modeling of bulk and single-cell
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functional genomics, CRISPR-based screening, or single-cell technologies. Hands-on experience with next-generation sequencing library prep and tissue culture models. Demonstrated ability to conduct independent
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understood, modeled, and ultimately reversed or rebuilt using bioengineering and synthetic biology approaches. The successful candidate will: Investigate mechanisms of T-cell development, aging, and thymic
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-cell compartment, develops and deteriorates with age, and how these processes can be understood, modeled, and ultimately reversed or rebuilt using bioengineering and synthetic biology approaches
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, and interpreting models, Analyzing genetics data (e.g. GWAS, eQTLs), including predicting variant effects, stratifying patients, identifying desired patients for recall, Designing, synthesizing, and
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members on combinatorial CRISPR screen strategies for target credentialing in cancer models Computational analysis of screen data Design and execution of combinatorial target validation experiments in PDXO
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healing. The Granville research program spans basic molecular biology and biochemistry through to target validation, proof-of-concept in animal and human models, and collaborations with clinicians and
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human models, and collaborations with clinicians and industry. Dr. Granville’s laboratory has expertise in established mouse models of chronic inflammatory diseases related to wound healing, aging, injury