Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Research Specialist Scientist – Department of Surgery
University of California, San Francisco
Cardiovascular disease (CVD) is the leading cause of death, morbidity, and mortality in the United State. When it comes to cardiovascular disease, worldwide, women continue to have poorer outcomes than men. The causes of these discrepancies have yet to be fully elucidated. Precision Cardiovascular Medicine (PCM) refers to the customization of CVD care for patients to maximize the success of preventive and therapeutic interventions by using patient-specific information such as demographics (e.g. gender, race) and genetic data. We are looking for a Specialist at the Assistant, Associate, or Full level to assist with clinical data research effort in PCM. This individual will work on the state of the art data analytics and machine learning to risk stratify the UCSF cardiovascular patient population in integration with genetic information. Our analytics will be applied to the already-established and powerful platform that includes: the UCSF Electronic Medical Record (EMR), the UCSF STARGEO Precision Medicine Platform based on Gene Expression Omnibus (GEO). As an implementation of clinical care, this research’s goal is to improve precision diagnosis and ultimately management of CVD for both early detection and identification of patients at risk for rapid progression of the disease. With such a large patient base and the infrastructure for data abstraction in place at UCSF, we will be able to better describe the impact of these discrepancies within individual patients to reduce them. It is an outstanding opportunity for those wanting more exposure to the field of clinical data research.
The research specialist key responsibility includes:
• Create a precise knowledge base of the entire patient population for CVD based on UCSF data.
• Data analytics on CVD cohort with state of the art statistical and Machin Learning models.
• Precision annotation of digital samples in NCBI’s gene expression omnibus (GEO) with STARGEO.
• Test and fine-tune the prediction models to provide a guideline for providers for personalized medicine.
• Publish the results in high impact journals and contribution in writing the grants.
• Ph.D. in Bioinformatics with at least 3-5 years postdoc experience in biomedical informatics research; 3-5 years of research experience in a community-or academic-based Biomedical and computational healthcare system.
• Demonstrated capacity to work accurately and learn quickly.
• Excellent written and oral communication skills, as well as meticulous organizational skills, are required.
• Significant and comprehensive research portfolio focused on clinical data analytics.
• Demonstrated experience collaborating with researchers from various research disciplines in identifying and researching opportunities for improving care; successful record of collaboration and mentoring.
• Successful and sustained track record in publishing in the peer-review journals.
All applicants with a relevant background may apply, but a priority will be given to applicants with clinical data analytics experience. This is an outstanding opportunity to learn a variety of research techniques used in clinical data research to improve health outcome while working in an outstanding institution.
UCSF seeks candidates whose experience, teaching, research, or community service 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.
Please apply online at https://aprecruit.ucsf.edu/JPF02708.
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