The postdoctoral fellow will develop and apply computational methods utilizing genetic and omics data to understand health and disease phenotypes. The position is in the Sofer lab, within the program of Sleep Medicine Epidemiology at the Harvard Medical School and Brigham and Women’s Hospital. Current areas of methodological applications in the group include genetic association analyses, variance components models, and polygenic risk scores development.  Data types include genetics, metabolomics, RNA-seq, proteomics, and methylation. Phenotypic domains of interest include sleep, cognitive function, and cardiometabolic phenotypes. Specific projects will depend on the fellow’s skills and interests. The fellow will work collaboratively with a diverse group of experts in statistics, bioinformatics, genetics, and epidemiology. They will have opportunities for mentorship of students. The candidate will ideally have background in handling large genetic data, familiarity with statistical models used in genetic association analyses, interest in the study of admixed populations, and in developing computational methods.

Interested candidates should send their curriculum vitae, contacts of three references, and a cover letter to Dr. Tamar Sofer [email protected].

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Doctoral degree in Computer Science, Statistics, Biostatistics, Bioinformatics, or a related field. Experience working with large genetic datasets. Interest in methodology development.