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-throughput functional genomics, and/or computational biology. Candidates should be highly collegial, hard-working, reliable, and curious, with a strong professional track record and excellent written and oral
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, an understanding of computational data analysis is highly encouraged and supported. Research activities may include: Culture and maintenance of mammalian cancer cell lines and our established pre-clinical platform
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). Maintain rigorous experimental documentation, data management practices, and reproducible analysis pipelines. Present results at lab meetings, departmental seminars, and scientific conferences; contribute
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histological and multiomic analysis of MS brain tissue to elucidate cellular and molecular mechanisms underlying demyelination and neurodegeneration. The laboratory is embedded within a highly collaborative
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analysis, kinetic modeling, data analysis, and results dissemination. Qualifications: The ideal candidate will have some prior hands-on experience in preclinical PET/CT or PET/MR imaging, image processing
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control outcomes, and quantitative bias analysis. Lead the preparation of first-authored manuscripts for peer-reviewed publication, and of abstracts and presentations for national and international meetings
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projects. The research associate will develop expertise in retinal imaging, experimental design, data analysis, scientific writing, manuscript preparation, and presentation of research findings, supporting
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cultures and brain organoids. Molecular and cellular biology approaches, including gene-expression analysis and characterization of cellular phenotypes. Genetic and genomic approaches, including gene editing
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and PBWT-based algorithms (RaPID, RAFFI, FiMAP, ROH analysis, local ancestry inference), now extending into GBWT/RLBWT-based pangenome indexing, efficient pangenome graph construction and query, cross
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impactful scientific projects and manuscripts. The successful candidate will contribute to multi-center projects that integrate population science, molecular epidemiology, and large-scale cohort analysis