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finite element methods, leveraging existing and actively developed libraries in AFRL related to crack growth. 4. Employ the hybrid FEM-ML framework to demonstrate a machine-learned crack model based
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instability in human stem cells, disease, and cancer, using human pluripotent stem cells and targeted differentiation approaches as a primary working model. Our goal is to determine how DNA and RNA instability
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for such complex systems, one needs to advance research in a number of areas within scientific computing, including: visualization, simulation, and modeling. The SCI Institute presents a highly challenging
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population-based research and statistical modeling. Strong publication record and scientific writing skills. Proficiency in statistical software (e.g., R, SAS). Ability to work independently and
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. · Experience in population-based research and statistical modeling. · Strong publication record and scientific writing skills. · Proficiency in statistical software (e.g., R, SAS
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you with the unique opportunity to drive research projects, employing a cross-disciplinary approach to immunology, genome editing, and omics techniques, as well as work with transgenic mouse models
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Research and Training Institute (CVRTI), is seeking a motivated postdoctoral fellow to work on projects that bridge from basic cell biology to small and large animal model systems. The focus of the lab is
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conditions related to aging. Specifically, this project will design models to develop biomarkers—particularly those related to blood vessels—to forecast clinical outcomes. This project will leverage unique
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, the cell biology of photoreceptors and hair cells, and potential treatments. We utilize mouse models and various approaches in molecular and cellular biology, biochemistry, omics, bioinformatics
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the application process will be provided to individuals with disabilities. The University of Utah is an Affirmative Action/Equal Opportunity employer and does not discriminate based upon race, ethnicity, color