Postdoctoral Research Fellow / Research Associate Position in Data Science
Postdoctoral Research Fellow / Research Associate Position in Data Science and Smartphone-Based Digital Phenotyping
School Harvard T.H. Chan School of Public Health
The Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, is seeking candidates with a Ph.D. in biostatistics, applied mathematics, statistical physics, computer science, or a related quantitative field for a two-year Postdoctoral Research Fellow or Research Associate position to work on a digital women’s health study. For general details of the study, including study investigators, please see https://www.hsph.harvard.edu/news/press-releases/harvard-apple-nih-study/.
The researcher will work as part of a large interdisciplinary team consisting of epidemiologists, clinicians, biostatisticians, computer scientists, and biomedical engineers to develop and apply methods to data collected by a smartphone or wearable device. This is a very exciting research area for anyone with a serious interest in temporally dense, high-dimensional data and its applications in women’s health. Methodologic challenges within this context will include dealing with missing data and dropout, the development of multi-stage sampling strategies for validation sub-studies, as well as the potential to develop novel data integration methods that link survey and passively collected data with other data types.
Doctoral degree in biostatistics or statistics, computer science, applied mathematics, statistical physics, or a related quantitative field. Excellent programming skills in Python is essential, and familiarity with big data analysis frameworks, such as Apache Spark, is advantageous. Must be able to work independently and in a team environment and must have strong communication skills. Appointment at the research fellow or associate will depend on prior experience of the successful candidate.
Academic questions regarding this position can be sent to JP Onnela (firstname.lastname@example.org)
Contact Email email@example.com
Equal Opportunity Employer
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
Minimum Number of References Required 2
Maximum Number of References Allowed 5