Postdoctoral Fellow - Harvard T.H. Chan School of Public Health

Cambridge, Massachusetts (US)
Nov 06, 2020
End of advertisement period
Feb 04, 2021
Contract Type
Fixed Term
Full Time

Postdoctoral Fellow

School    Harvard T.H. Chan School of Public Health
Department/Area    Department of Biostatistics/Quantitative Cancer Biology and Analysis

Position Description    
We are seeking a candidate with expertise in computational and systems biology to work as part of a multidisciplinary team developing methods relevant to the study of genetics, gene regulatory networks, and the use of quantitative imaging data as biomarkers. Our goal is to use these methods to better understand the development, progression, and response to therapy. The successful applicant will work directly with Dr. John Quackenbush, but will be part of a community of researchers consisting of Dr. Quackenbush, Dr. Kimberly Glass, Dr. John Platig, and Dr. Camila Lopes-Ramos, and members of their research teams.

Basic Qualifications    
A PhD in computational biology, biostatistics, applied mathematics, physics, biology, or related fields and demonstrated skill in methods and software development and the analysis of biological data are required.

Additional Qualifications    
The ability to work as part of a large, integrated research team and strong verbal and written communication skills are essential. Previous work in cancer biology/cancer genomic data analysis is welcome but not required.

Special Instructions    
Administrative questions regarding this position can be sent to Nicole Trotman at ntrotman@hsph.harvard.edu.

Scientific questions regarding this position can be sent to John Quackenbush at johnq@hsph.harvard.edu

Contact Information    
Nicole Trotman

Contact Email    ntrotman@hsph.harvard.edu

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 

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