Data is vital to advance clinical knowledge across a spectrum of medical and surgical fields. Are you ready to transform data discoveries into valuable information, which will empower tomorrow's medicine? We invite you to consider and apply to this great opportunity today!
About the Team The Kao Autoimmunity Institute (KAI) is the first institute of its kind on the West Coast and was founded in 2019 through a generous donation from the Kao Family Foundation. The Institute's mission is to advance the knowledge and treatment of autoimmune diseases through research driven by excellent patient care. The KAI's team strives to educate the next generation of autoimmunity‑focused clinical and translational investigators.
To learn more, please visit the Kao Autoimmunity Institute (KAI) website .
The Research Bioinformatician I will perform general bioinformatics analysis and software support for projects involving in omics. The RB I member will manage omic data including loading and querying data from database systems and public repositories, and transforms and merges multilevel omic data into user-friendly formats. The incumbent assists in development, testing, and maintenance of modular software pipelines for genome sequencing, provides daily bioinformatics data analysis, preliminary interpretation of the data, and reports of the results for possible publications.
Primary Job Duties and
Responsible for data analysis and software compilations including next generation sequence alignment, polymorphism identification, expression analysis, and visualization tools and browsers. Summarizes data analyses results in the form suitable as the basis for the first draft of written reports, and makes preliminary interpretations of the data. Assists with assembly, annotation, metagenomic analysis, and genotyping using high-throughput sequencing platforms including 454, Illumina, and Pacific Biosciences.
Manages and redefines data to the necessary formats. Helps to identify, evaluate, and incorporate relevant algorithms and software pipelines by reviewing pertinent literatures in bioinformatics and computational biology.
Bachelor’s Degree in Computer Science, Electrical Engineering, Computational Biology, Bioinformatics, or relevant fields (e.g., Biology with strong quantitative training, Biostatistics with concentration in Bioinformatics) is required.
Experience in a research environment is highly preferred. Background and work knowledge in algorithms, scientific computing, and machine learning or statistics is preferred. Proficiency with C/C++, Java, Perl, python, and the Unix (Linux) environment.
Experience in manipulating, analyzing, and annotating very large genomic (e.g. NGS) data sets, both in exploratory and pipelined fashions is highly preferred.