Professional Researcher Computational Biology in translational breast cancer research
Candidate MUST have a PhD in Bioinformatics or Computational Biology or Comparative Biochemistry or related field with several years of experience including machine learning and programming, track record of completed projects and manuscripts, and looking to apply their skill set in a clinical setting for translational research, preferably breast cancer.
The main responsibilities will be to analyze next-generation sequencing data from serial samples of breast cancer patients with significant residual disease after neoadjuvant targeted and/or chemotherapy from the I-SPY 2 TRIAL. I-SPY 2 is a multicenter standing platform trial for women with high risk early stage breast cancer. The primary endpoint is pathological complete response (pCR), defined as absent of invasive tumor in the breast and the nodes at the time of surgery. I-SPY 2 is a biomarker rich trial, where tissue and blood samples and MRI-imaging parameters are collected serially over the course of therapy. The candidate will work in the context of the Bioinformatics and Biostatistics Core (UCSF Core Lead: Christina Yau, and Computational Scientist Denise Wolf) of a funded P-01 proposal on Project 3 to evaluate the biological features characterizing residual disease and the evolution of these characteristics to potentially inform treatment decisions (Project PI: van’ t Veer, P-01 PI: Esserman and Hylton). Candidate is expected to support the P-01 work, but is encouraged to develop additional projects (e.g. novel algorithms for integrated analysis of our wealth of I-SPY 2 biomarker data and related projects). Candidate is encouraged to develop an independent research line and an academic career.
Candidate must have direct experience working on projects utilizing current methodologies to perform the following analyses:
Next generation sequencing pre-processing pipelines (including alignment and mutation calling and QC)
Clonality analysis of serial samples
Copy number analysis based on sequencing data
Functional significance analysis of somatic alterations
Preferred experiences include:
Knowledge of Breast Cancer etiology and progression; drug resistance
Working with RNA-sequencing data (including pre-processing)
Integrating DNA and RNA sequencing data for calling of expressed mutations
Allelic frequency estimation
Population structure inference modeling
Integrating somatic alterations in a pathway-based approach
Experience with high performance computing resources such as Cloud and running pre and post processing workflows in cluster
Machine learning computational analysis
Contact for further information: Laura van ‘t Veer, PhD (Professor, Department Laboratory Medicine, UCSF) at firstname.lastname@example.org.
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.
Internal Number: JPF03128
About University of California, San Francisco
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.