We are seeking a highly motivated individual to join our team as Bioinformatician in the Diabetes Center at the University of California, San Francisco. The center is headed by Professor Matthias Hebrok, Ph.D.
The Diabetes Center at UCSF has one mission: to advance the care and treatment of patients with type 1 and type 2 diabetes worldwide so that we may achieve the ultimate goal of curing the disease. Research focuses on three central areas: Beta cell/stem cell biology, immunology and inflammation/obesity/metabolism. In addition, our team includes physicians directly involved in patient care and clinical trials. We recognize that basic science investigations and clinical investigations both increasingly benefit from high throughput approaches that include genome sequencing, transcriptomics, epigenomics, proteomics and metabolomics. We are recruiting a specialist who will help build robust analytic pipelines for systems-scale datasets relevant to the core aims of the Diabetes Center. The Data Analyst will work in a highly collaborative environment, with biologists, statisticians, bioinformaticians, and medical doctors.
The specialist will be responsible for enabling bioinformatic analysis for members of the Diabetes Center. She/he will develop pipelines to be used by multipe labs for analysis of multiple data types including (but not limited to) genome/exome-sequencing, RNA-Seq, bisulfite-sequencing, ATAC-Seq, ChIP-Seq, mass-spectrometry, flow cytometry and mass cytometry. The ideal applicant should be proficient in the data analyses and have expertise in integrating multiple datsets to arrive at biological insights. Responsibilities will include maintenance of server space, user interfaces , and in training of Diabetes Center members in relevant analysis methods.
Required qualifications: • PhD (or equivalent) in biostatistics, statistics, bioinformatics, computer science or a related quantitative field. • Proficiency in R, PERL, Python • Strong knowledge of statistical methods including Cox models, logistic regression, linear regression, and elastic net regression. • Strong knowledge of parametric and non-parametric statistics and common statistical tests. • Excellent English communication skills, both written and oral, strong organizational and documentation skills, and excellent interpersonal communication are essential.
Preferred qualifications: • Experience managing large and complex datasets. • Experience with machine learning techniques, such as random forest models and support vector machines. • Experience with microarrays and sequencing. • Familiarity with clinical study design. • Experience with pathway analysis, such as GSEA or DAVID.
Screening of applicants will begin immediately and will continue as needed throughout the recruitment period. Salary and rank will be commensurate with the applicants experience and training. Please apply online at https://aprecruit.ucsf.edu/apply/JPF01031
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.
The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and high-quality patient care. It is the only UC campus in the 10-campus system dedicated exclusively to the health sciences.