Postdoctoral Fellow

South Carolina Biomedical Data Science and Informatics (BDSI) program seeks to fill NLM training grant-funded postdoctoral positions. BDSI (established in Fall of 2017) is an interdisciplinary and joint program between the Medical University of South Carolina (MUSC) and Clemson University. BDSI program applies concepts and methods from computer science and other quantitative disciplines together with principles of information science to solve challenging problems in biology, medicine, and public health. Specialized tracks currently include precision medicine, population health, and clinical and translational informatics. The program is a unique collaboration for transformation of the health systems of South Carolina that leverages Clemson University’s strengths in computing, engineering, and public health and MUSC’s expertise in biomedical sciences and healthcare delivery. The program is based on integrating core competencies from the fields of biomedical informatics (Kulikowski et al. 2012, Valenta et al. 2018) and data science (Margolis et al. 2014). The NIH/NLM T15 program currently supports 18 top biomedical informatics and data science academic programs in the USA. This position is funded by a T15 award SC BIDS4Health to MUSC and Clemson University. We offer team mentorship by focusing on unique and distinct needs of each trainee using robust individual development plans. BDSI faculty at Medical University of South Carolina and Clemson University have diverse academic and clinical expertise and established academic records. Under the guidance of our faculty the trainees will develop analytical, academic, management, and mentorship skills to further advance their careers. The position comes with support for travel to scientific meetings and workshops and additional funding for research training-related expenses. SC BIDS4Health strongly encourages applications from all eligible individuals. Postdoctoral candidate selection criteria: 1) prior research productivity, 2) research interest and experience, 3) quality and performance during prior training, and 4) recommendations of predoctoral mentor or previous postdoctoral mentor. All candidates will be evaluated for their commitment to a career in academic research relevant to serving rural and underserved populations by application of biomedical informatics and data science. Details of such commitment has to be included in the Research Statement.