Postdoctoral Fellow, Medical Physics/Biostatistics

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Postdoctoral Fellow, Medical Physics/Biostatistics

Memorial Sloan Kettering Cancer Center (MSK) is one of the world’s premier cancer centers, committed to exceptional patient care, leading-edge research, and superb educational programs. The blending of research with patient care is at the heart of everything we do. The institution is a comprehensive cancer center whose purposes are the treatment and control of cancer, the advancement of biomedical knowledge through laboratory and clinical research, and the training of scientists, physicians and other health care workers.

We are seeking a highly motivated candidate, interested in biostatistics and bioinformatics, for a position as a postdoctoral fellow. This position will focus on machine learning-based modeling of outcomes on genome-wide association studies (GWAS) that include radiation and genetics (e.g., the Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study and REQUITE Study).

 

The ideal candidate should have:

  • PhD degree in bioinformatics, computer science, biostatistics, statistics, physics or a related field
  • Experience in predictive modeling using machine learning techniques
  • Experience in analyzing large data sets, be proficient in at least one of the statistical programming languages R/Matlab/Python and have experience working on Unix/Linux systems and basic shell scripting

 

Experience in GWAS data analysis would be advantageous, but is not necessary.

 

Please send CV, a letter outlining your interest and names/contact information of three references via email to:

Jung Hun Oh, PhD

Assistant Attending, Department of Medical Physics

ohj@mskcc.org

 

MSK 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.

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