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, with a focus on combination therapies that account for cell-cell interactions. Experimental model systems will include cancer cell lines, organoid/assembloid models, and clinical tumor samples. Projects
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care actually happens, and how it can be made better. This is a role for someone who’s excited to work with big, messy, real-world data — and who wants to do more than just build models. We’re looking
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combinatorial panning methods, including phage and mRNA display, to identify de novo peptides for promising biomarkers lacking a natural ligand or lead structure. We then optimize peptide ligands for affinity and
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to evaluate and model the health consequences of emissions of NOx, benzene, CO, and additional gaseous pollutants from natural gas stoves and other appliances. The exposure measurements and modeling will build
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labs at Stanford to tackle emerging clinical questions in oncology, utilizing various AI methods, predictive modeling approaches, and large language models. Specific areas of interest include but are not
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of evidence-based tools that support students and teachers while, simultaneously, advancing the frontiers of knowledge through inclusive research at an unprecedented scale. ROAR empowers educators, families
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Lab at Stanford University School of Medicine is seeking a highly motivated postdoctoral fellow to join our team focused on advancing the understanding and efficacy of tumor-infiltrating lymphocyte (TIL
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patients and established pipelines for data pre-processing, analysis, and integration (Good et al. Nat Med 2022; In Preparation). We now seek to build generative artificial intelligence (AI) models
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chromatin structured contexts. Ideal for those with experience in cell culture, molecular biology, and seeking to acquire or strengthen expertise in gene editing methodologies, bioinformatics, and DNA DSB
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individual development in the context of structural racial and economic inequality using a variety of theoretical and empirical approaches responsive—centering the needs of children, families, the early