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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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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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record (EHR) data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the United States. Through the VA Corporate Data Warehouse, we have curated the VACS-National
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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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Develop computational pipelines and machine learning methods for genomic, clinical, or imaging data analysis. Analyze large-scale biobank, EHR, and/or imaging datasets. Apply statistical, deep learning, and
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associate will lead and contribute to the analysis of behavioral task data using computational modeling approaches, working closely with computational psychiatry experts Drs. Xiaosi Gu and Robb Rutledge and
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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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: Comparative genomics RNA secondary structure prediction Covariance models and RNA homology search methods Machine learning or artificial intelligence applied to biological data Transcriptomics analysis methods