84 algorithm-development-"Prof"-"Prof"-"Prof" Postdoctoral positions at University of Washington
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for professional development in emerging techniques and interdisciplinary methodologies. • Access to state-of-the-art facilities and resources at the University of Washington. • A commitment to mentorship and career
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. They will contribute to multi-disciplinary research on Alzheimer's disease, multiple sclerosis, and other CNS conditions, and develop innovative AI-powered approaches for data analysis and MRI acquisition
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and how this interaction influences the virus evolution and its maintenance in nature. In addition, the Lopez laboratory seeks to discover determinants of acute and chronic respiratory virus
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the areas of either tumor immunology, the impact of fibrosis on tumor immunology or developing new approaches for tumor immunotherapy. The DeNardo lab uses state of the art techniques in single cell genomics
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seeding. Identify cancer-derived genetic drivers in T cell-cancer cell interactions in the immune microenvironment. Develop novel single cell technologies to link cancer-stroma transcriptome within
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dedicated to professional and career development. Preferred Qualifications: Ph.D. in Microbiology, Cell biology, Biochemistry or other related discipline. Extensive expertise in mammalian cell culture and
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Developing models to represent structure in networks using low dimensional manifolds Modeling demographic and health trends in low-resource settings Developing a decision-making framework for policy decisions
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environment to develop and foster independent careers. Successful candidates will have the opportunity to use novel mouse models and cutting-edge multi-omics approaches to understand molecular mechanisms with
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at WashU School of Medicine in St. Louis. The Klechevsky lab is dedicated to understanding how dendritic cells and other myeloid cells influence the development of immunity and cancer. Our broad and
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models