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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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; radiopharmaceutical dosimetry with novel agents and/or software techniques; alpha particle RPT radiation biology modeling and analysis of clinical trial data. For additional information on a subset of expected
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therapies for such cancers, and test them in cellular and animal models. If you thrive in a collaborative environment and have a keen interest in both imaging and treatment of cancer, this role is perfect
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Project 2: Experience working with large datasets and machine learning Reasonable proficiency in any coding language used in data science Mixed-methods research experience Online Application Required
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for inpatient consultation/liaison service are also available. The Pediatric Neuropsychology track focuses on psychological assessment of learning and attention disorders and neuropsychological testing with youth
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. Desirable Qualifications: Desirable Qualifications: Laboratory skills:in vitro and in vivo studies, flow cytometry, andtechniques in molecular biology Experience with mouse models. Competency in SolidWorks
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: Desirable Qualifications: Laboratory skills: in vitro and in vivo studies, nanoparticle drug delivery, and RNA therapeutics Experience with mouse models. Competency in SolidWorks, Graphpad, and Adobe
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on human medicine by studying the impact of novel therapy, screening, or treatment models. Several projects are available and focus on topics such as a pilot project studying lung cancer screening in
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experiments Design guide RNAs and use CRISPR for gene editing Design AAV vectors for gene therapy on mouse models Capture fluorescent images and quantify data Purify proteins and perform western analyses
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function. The candidate will participate in an integrated set of projects aimed at developing a model of the normal human lung against which individuals can be compared to identify and evaluate early