A full-time one-year post-doctoral training fellowship (with possibility of a one-year extension) for PhDs and MDs to bridge the gap between data science and clinical medicine. Fellows will receive full salary support from the program. The focus of the program is to immerse fellows in interdisciplinary research and clinical workflows so they can learn, build and deploy real-world health data solutions. The fellowship will be self-directed and project-based, with top researchers and clinician mentors.
Ideal fellows are highly motivated, detail-oriented individuals with experience in genome data analysis, large-scale data visualization, artificial intelligence and machine learning. They will join a cohort developing computational tools and services for the analysis, modeling and management of biomedical data in patient care. Fellows will be responsible for building, modeling and testing algorithms and visualization tools informing the clinical management of patients from large-scale clinical laboratory datasets and patient electronic medical records. Competitive applicants will include individuals from varied training backgrounds, such as medicine, biosciences, engineering and computer science. Fellows with strong computational, analytical and statistical training and bioinformatic and machine learning backgrounds are preferred. A PhD or MD is required.
For more information about the program, kindly visit https://med.stanford.edu/patho...
We invite eligible applicants to submit:
Please submit all materials and letters electronically (pdfs) by May 1, 2023 to:
Vivek Charu, MD, PhD
Assistant Professor of Pathology (Quantitative Sciences)
Assistant Director, Clinical Data Fellowship Program
Email: [email protected]
Mar 08, 2023