We are seeking a highly motivated postdoctoral fellow in biostatistics who is interested in statistical and computational methods development and application. This position will provide exciting opportunities to work with large-scale multi-center telehealth data and develop novel methods for causal inference, machine learning, data integration, and precision oncology. Successful candidate will work on development, as well as applications of these methods and will participate in knowledge generation and will be expected to publish findings in top-tier statistical and biomedical literature. This position will be supported by MATCHES (Making Telehealth Delivery of Cancer Care at Home Effective and Safe), a new NCI-funded center (https://grants.nih.gov/grants/guide/rfa-files/RFA-CA-21-029.html) dedicated to building the necessary evidence base to guide the appropriate role of telehealth in the delivery of cancer care. A multi-disciplinary team of investigators at MSK has received a 5-year award to support this work and will launch this project in the fall of 2022.
Applicants should hold a Ph.D. degree in biostatistics, statistics, machine learning, or a related field. The successful candidate should have solid methodological training in statistics, be comfortable working with large data sets, be proficient in R and/or Python, and have experience working on Unix/Linux systems. Prior experience with telehealth data, causal inference, and precision medicine would be an asset.
The successful applicant will be supervised jointly by Drs. Yuan Chen, Katherine Panageas, and Mithat Gönen. This position is open immediately. The initial appointment is for two years with a possibility for renewal. To apply, please send a cover letter, CV, research statement (optional) and the names of 3 references by email to Samantha Vasquez at [email protected].
MSKCC is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, disability, veteran status or any other factor which cannot lawfully be used as a basis for an employment decision.