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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
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. Preferred: Integration of wet lab findings with computational analysis of multi-omic data (e.g., structural variants, transcriptomics) using R or Python pipelines. Mentoring and Professional Development
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cohort covering inpatient and outpatient diagnoses, dispensed inpatient and outpatient prescriptions, laboratory results, vital signs, clinical notes, and structured health factors including alcohol
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construction, data harmonization, statistical analysis, and reporting. Mentoring and professional development The postdoctoral associate will participate in regular one-to-one meetings with the PI, weekly
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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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mentorship and structured educational activities, trainees will gain hands-on experience generating, analyzing, interpreting, and experimentally validating functional genomic data within biological and
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data through site or phone intakes, library research, structured interviews, or through other means for designated research assignments Records and compiles information related to research data. Codes
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-based analysis of research data as a meta-domain and discovery of relevant datasets through both graph search and guided browsing through relationships. Along with Yale managed datasets, these will also
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-related mindsets; the mechanisms and moderators of emotion-focused interventions; and the design and analysis of large-scale digital and app-based research. The position also includes a role in supporting
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mammalian riboswitches and defining their biological functions. Research activities may include: Genetic manipulation of mammalian cell lines using CRISPR-based approaches Construction and analysis