A postdoctoral position is available in the Rudrapatna Laboratory within the Bakar Computational Health Sciences Institute at the University of California, San Francisco (UCSF). The research goal of the lab is to develop methods for the optimal repurposing of electronic health records (EHR) data to support multiple use cases – epidemiology, clinical decision making, healthcare reimbursement, drug and device regulation – all under the umbrella of a field now known as real-world evidence. Although the primary clinical interest of the group is in Inflammatory Bowel Disease, the approaches being developed are quite general and future projects will extend across the spectrum of diseases seen and treated at UCSF. Two methodologic themes that underlie the group’s aspirations are: 1) use of text mining techniques like information extraction/retrieval and knowledge base generation to augment existing EHR structured data assets, and 2) the integration of retrospective and prospective study designs. This position is initially available for one year with a possible extension for up to two years based on performance evaluation.
The Rudrapatna Laboratory is embedded in the Bakar Institute (bakarinstitute.ucsf.edu) at the UCSF, a world-class health system and biomedical research university, and the top public recipient of NIH funding for the past 13 years straight. Postdoctoral fellows will work in a richly stimulating environment with ample access to expertise across domains, including epidemiology, biostatistics, data science, clinical informatics, and clinical areas across the full spectrum of medical and surgical specialities. Posdoctoral fellows will divide their time between a brand new, state-of-the-art building (wgvcv.ucsf.edu) in the Mission Bay neighborhood of San Francisco and working remotely. The Bakar Institute features access to unique clinical data assets, including a de-identified extract of the complete EHR at UCSF, a machine redacted extract of the complete corpus of clinical notes authored at UCSF (80M+), as well as a cross-campus database covering over 5 million patients in order to enable multi-center studies. Other computational resources include access to a high performance computing cluster (wynton.ucsf.edu) and GPUs for deep learning on clinical data.