BIOINFORMATICS PROGR

BIOINFORMATICS PROGR

San Francisco, CA Job Details 8 hours ago Qualifications Collaborate with healthcare professionals Research design Data Integration (Data management) Next generation sequencing Databases Managing IT infrastructure Experimental design Research data analysis Implementing machine learning algorithms Senior level Bioinformatics data analysis Full Job Description The Cardiovascular Genetics Center at University of California, San Francisco is seeking an experienced bioinfomatician to faciliate several lines of research that relate broadly to inherited forms of cardiovascular disease, regulation of gene expression in health and disease, and personalized genetic medicine. The incumbent will work closely with prinicpal investigators within the Genetic Center to design and implement analysis pipelines in several broad areas of computational biology including analysis of experimentally generated datasets in basic research, and analysis of patient-derived genomic data from UCSF patients as well as publicly available data repositories. The goal will be to make fundamental biological discoveries that can ultimately be translated to understanding, prevent, and treat heritable cardiovascular diseases. More specifically, the role involves developing and utilizing computational tools and systems to analyze and interpret biological or other research data. Utilizes and develops algorithms, computational techniques, and statistical methodologies. Helps in the design of new experiments. Implements end-user needs in database searching and integration. Maintains the computational infrastructure and tracks the flow of samples and information for large-scale studies. Provides web-based bioinformatics and access to public and proprietary databases. The scope of work will include anayzing biological datasets generated experimentally including but not limited to bulk and single cell multiomics sequencing, epigenomics data including ChIP-Seq and CUT&RUN, and imaging data including spatial transcriptomics; overlapping experimental data with publicaly available genomic and expression datasets including UK Biobank, All of Us, Heart Cell Atlas, and similar datasets. There will also be analysis of genome sequencing data including whole exome- and whole genome sequencing to discern rare and common variants implicated in cardiovascular disease.

The incumbent will also implement machine learning algorithms to analyze biological datasets. There will be frequent collaboration with bench scientists and clinicians in a highly collegial and stimulating scientific environment.