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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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a related field Demonstrated ability to design, execute, analyze, and communicate rigorous research Scientific curiosity about how small molecules are transported, interconverted, compartmentalized
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experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
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-disciplinary sources across history, anthropology, organizations, design, law, business, etc.; (c) producing an interactive web-based platform to enable open-ended exploration of that database; (d) collaborating
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, the Peabody Museum, or more technical parts of the University for periods of time to learn about both research and operational workflows. Connections with the Wu Tsai Institute, the AI at Yale program, the Data
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Postdoctoral Associate | Mitochondrial Genomics | Lake Lab — Yale University, Department of Genetics
implement a mentorship plan tailor-made for their goals and interests. Mentorship will be provided for fellowship applications, manuscript writing, presentations, and independence. The successful candidate
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emphasis on mentorship and value the opportunity to train future leaders in human genetics. The PI will work with the successful candidate to develop and implement a mentorship plan tailor-made
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causal inference, machine learning, and artificial intelligence is desirable ● Experience with clinical, EHR, or biobank data analyses is desirable Application Instructions To apply: Interested