Computational Biologist, Laboratory for Genomics Research (LGR)

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Job description

About the LGR

The Laboratory for Genomics Research (LGR) is a three-way partnership among UC Berkeley, UCSF and GSK to join forces together under one roof in an immersive collaborative R&D environment to discover and validate novel therapeutic targets and bring better medicines faster to patients with unmet medical needs. The LGR strives to build advanced CRISPR-based genome editing and functional genomics platforms empowered by human genetics and computational biology to discover drug targets with high probability of success, expand our knowledge on the human genome in various disease contexts through mechanistic investigation into previously unknown genes and their networks, and deliver innovative research tools that are optimized and scalable for both academic and drug discovery research labs across the partner institutes.

Multiple positions are available for the Computational Biology team at the LGR. A successful hire will work closely with scientists from both the Technology and the Biology teams as well as those from UCSF, UC Berkeley, and GSK in fast-paced, cross-functional collaborative research environments. The new hire will report to the Head of Computational team.

Responsibilities:

• Process, analyze, and visualize pooled and arrayed CRISPR screening, RNA-seq (bulk & single cell), ATAC-seq and other NGS multi-omics data to generate actionable insights and hypothesis for target and biomarker discovery and validation

• Develop custom data analysis pipelines that can be executed by other computational and genomics scientists

• Stay up to date with most current data analytic methods and algorithms and implement them selectively into internal analytic workflow.

• Communicate complex technical information and analytical results verbally and in writing to scientists with diverse backgrounds.

Required Qualifications:

• A MS in computer science, bioinformatics, statistics, physics, mathematics, engineering, or other quantitative science or five to ten years of experience in the relevant specialization to be appointed at the Associate rank.

• A PhD (or equivalent degree) in computer science, bioinformatics, statistics, physics, mathematics, engineering, or other quantitative science or ten or more years of experience in the relevant specialization to be appointed at the Full rank.

• Hands-on experience in one or more of scientific scripting languages such as R, Python, and Perl

• Knowledge of basic concepts in biology and statistics

• Candidates must meet the required qualifications at the time of appointment. Candidate's CV must state qualifications (or if pending) upon submission.

Preferred Qualifications:

• PhD in computer science, bioinformatics, statistics, physics, mathematics, engineering, or other quantitative science

• Experience working within collaborative, multidisciplinary teams.

• Expertise in a combination of two or more of the following technology areas:

• Hands-on experience in large-scale genomics, genetic and/or epigenetic data analyses, including single cell level, using existing and custom pipelines.

• Knowledge in common bioinformatic tools and databases and experience in developing custom computational analysis pipeline leveraging these resources

• Working experience with relational database (Oracle, MySQL, PostgresSQL)

• Solid understanding of statistical methods relevant to large scale analysis of biological data sets

• Knowledge of genome-wide genetic, genomic, pathway and network methods.

• Experience in projects related to immunology, neuroscience, or infectious disease.

Please apply online, with a CV and three reference contacts.

UC San Francisco seeks candidates whose experience, teaching, research, or community service that has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.