A full-time postdoctoral scholar position is available for a highly motivated scientist interested in biostatistical and machine learning predictive modeling for healthcare and treatment-related outcomes and health economics research. The postdoctoral researcher will have the opportunity to lead statistical analyses working with large administrative and medical record datasets and will work with the UCSF Medication Outcomes Center (MOC) director Dr. Rodriguez-Monguio and her team to generate new statistical and modeling approaches to better understanding health outcomes in older patients with Alzheimer disease and related dementias. The fellow will have opportunities to publish the work in professional journals and/or present data at professional meetings. The fellow will also be encouraged to take advantage of other training and learning opportunities within the Department of Clinical Pharmacy, the School of Pharmacy, and other related entities at UCSF. The initial appointment is 1 year with renewal for an additional 1 year by mutual agreement.
Applicants should have:
- A Ph.D. in Biostatistics, Bioinformatics, Health Informatics, Computer Sciences, or related field.
- Knowledge of statistical software packages such as R and SAS and understanding of programming languages such as Python.
- Prior experience modeling survival and longitudinal data.
- Experience with machine learning modeling and artificial intelligence (AI) techniques.
- Please send a CV, a short cover letter summarizing your research experience, and the names and contact information for 3 references to: [email protected]