We are seeking a highly motivated individual to join our team as a Computational Biologist in the UCSF Diabetes Center and the Parker Institute for Cancer Immunotherapy (PICI). This candidate will have a primary appointment in the labs of Drs. Jeffrey Bluestone and Mark Anderson with a joint appointment in the laboratory of Dr. Jimmie Ye at the Institute for Human Genetics (IHG).
The Diabetes Center and PICI have a collaborative program to leverage single-cell genomics to advance the care and treatment of patients with type 1 diabetes and cancer. Research focuses on two central biological areas: regulatory/effector T cell function and cancer immunotherapy. 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 single-cell genomic approaches. We are recruiting a specialist who will lead the development of robust analytic pipelines for the analysis of systems-scale genomic datasets relevant to the core aims of the Diabetes Center and PICI. The Computational Biologist will primarily reside in Dr. Mark Anderson’s and Bluestone’s joint lab space. These two groups have several important collaborative projects examining, on a single cell basis the gene expression and T cell receptor usage of autoreactive T cells in samples from mice and humans with Type 1 Diabetes. These groups have been at the forefront of determining the role of the thymus and peripheral regulatory compartments in the development and fate of such cells. The incumbent will also have opportunities in the lab of Dr. Jimmie Ye, a leading genomics lab with a core team of experimental and computational biologists. In addition, the specialist will work in a highly collaborative environment at the Parker Institute for Cancer Immunotherapy, with biologists, statisticians, bioinformaticians, and medical doctors.
The specialist will be responsible for developing analytic pipelines, implementing state-of-the-art computational methods, and bringing together diverse multiparameter high-throughput genomic datasets to enable deep data integration and knowledge discovery for members of the Diabetes Center and PICI. Datasets include (but not limited to) single-cell transcriptomics (scRNA-seq, nuclear scRNA-seq), immune repertoire sequencing (TCR), proteomics (Ab-seq and CITE-seq) and epigenomics (scATAC-seq). The ideal applicant should be comfortable with the analyses of > 104 cells and have expertise in integrating multimodal data to arrive at biological insights.
Required qualifications: • PhD (or equivalent) in biostatistics, statistics, bioinformatics, computer science or a related quantitative field. • Proficiency in R, PERL, Python • Experience working in a high-throughput computing environment • Experience analyzing large-scale (104 – 106) single-cell datasets (i.e. scRNA-seq, scATAC-seq or CITE-seq) • Strong knowledge of statistical methods including Cox models, logistic regression, linear regression, and elastic net regression. • Strong knowledge of parametric and non-parametric statistics. • Excellent English communication skills, both written and oral, strong organizational and documentation skills, and excellent interpersonal communication are essential.
Preferred qualifications: • Experience managing other large and complex datasets. • Experience with machine learning techniques, such as random forest models, support vector machines and deep learning. • Familiarity with clinical study design. • Proficiency in a high performance computing language such as C++, Julia and C. • Experience with collaborative projects with biologists and domain experts.
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.
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.