A postdoctoral fellowship opportunity is available at the University of California, San Francisco in the Hong laboratory (honglab.ucsf.edu). The Hong lab is part of both the Department of Radiation Oncology and the Bakar Computational Health Sciences Institute.
The Hong lab focuses on combining clinical domain knowledge with data science to generate insights from real world data, develop and implement actionable computational tools, and evaluate the benefit of these advances for personalized cancer care. We have a specific interest and expertise in machine learning, natural language processing, computational information retrieval, and imaging analytics. We apply these methods to identify new knowledge regarding clinical practice and patient outcomes (real-world evidence), make actionable predictions, and identify new interventions. Our lab works from end-to-end along the development and implementation pipeline to develop tools for clinicians to make a meaningful difference in patient care.
We are recruiting highly motivated investigators to develop algorithms and analyses with multi-modal clinical data to guide personalized oncology care. A current priority area is the application of natural language processing (NLP) on clinician- and patient-generated notes and correspondence. Projects will be tailored to the individual's prior experience and interests but will center on the utilization of computational techniques to drive clinical prediction or discover new clinical insights using routine clinical data (including electronic health records, imaging, digital pathology, and genomic data). This position is initially available for one year with a possible extension for up to two years based on performance evaluation.
The Hong 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.
The successful candidate will work closely with the principal investigator and join a multidisciplinary team of clinicians and scientists in the Department of Radiation Oncology and the Bakar Computational Health Sciences Institute. They will serve as the primary driver of two research grants focused on applications of NLP to 1) improve clinical algorithms to improve supportive care and 2) identify disparities in symptom management. The candidate will help assemble and consolidate data assets, collaborate closely with individuals developing gold-standard annotations for model training, perform modeling, and disseminate the results in the form of conference presentations and first-authored manuscripts.
The successful candidate is expected to additionally spend time developing an independent research program. Grant submission is an important part of the training, and the candidate will be expected to support PI submissions as well as initiate his or her own applications.
Qualifications
Essential Qualifications:
- A PhD (or equivalent) in one of the following fields: computational biology, biostatistics, epidemiology, bioinformatics, data science, computational linguistics, computer science, or machine learning. MDs are welcome to apply if they have a strong background in one of the above fields.
- A strong interest or background in clinical research and epidemiology
- Experience in Python or R. SQL is strongly recommended.
- Excellent communication skills and a track record of peer-reviewed first-authored publications
- A high degree of motivation and ability to operate independently
Desired Qualifications:
- A background in natural language processing (NLP) and associated tasks, including text classification, information and relation extraction, knowledge representation. A specific background in clinical NLP would be valuable.
- A background in clinical informatics, including knowledge of the OMOP common data model. Experience using clinical databases, especially the Epic EHR database backends (Clarity, Caboodle) would be valuable.
- A background in causal inference
- A background in machine learning. A specific background in deep learning would be valuable.
Qualified candidates should email a statement of research interests, curriculum vitae, and list of three references as a single PDF to Dr. Julian Hong at [email protected] with “Postdoc application” in the subject line.