HARVARD UNIVERSITY

Postdoctoral Research Position in Computational Biology and Environmental Health

Location
Cambridge, Massachusetts (US)
Posted
Nov 23, 2020
End of advertisement period
Jan 23, 2021
Academic Discipline
Life sciences, Biological Sciences
Contract Type
Fixed Term
Hours
Full Time

Postdoctoral Research Position in Computational Biology and Environmental Health

School    Harvard T.H. Chan School of Public Health
Department/Area    MIPS/Environmental Health

Position Description     
The Haber Lab in the Department of Environmental Health at Harvard T.H. Chan School of Public Health has an opening for a highly motivated Postdoc. Projects involve developing and applying computational and statistical approaches to investigate the effect of environmental exposures on the lungs and effects on the pathogenesis and exacerbation of asthma. The successful candidate will join an interdisciplinary team spanning several institutions, including the Chan School at Harvard, Brigham & Women’s Hospital, and the Broad Institute of MIT and Harvard.

We seek an enthusiastic post-doctoral researcher to develop novel computational approaches for analysis of asthma at either the molecular level using computational and systems biology approaches to analysis of single-cell RNA-sequencing data, or at the epidemiological level, using geospatial analysis of city-wide datasets.

At the molecular level, projects in the lab examine the cellular and molecular effects of environmental exposures on the airways, and focus on analysis of high-dimensional ’omics data (particularly single-cell RNA sequencing) from clinical samples and mouse models of airway injury, inflammation and regeneration. The successful candidate will also collaborate closely with clinical pulmonologists and immunologists to study molecular mechanisms underlying both airway tissue homeostasis and asthma pathogenesis.

At the epidemiological level, projects examine increased risk that neighborhoods and individual housing units contribute to asthma burden in Boston and elsewhere. This line of research in the lab seeks to isolate factors, such as poor housing conditions, in the social, physical, and built environment that contribute to racial and socioeconomic disparities in rates of asthma.

Basic Qualifications    
PhD or equivalent in computational biology, computer science, epidemiology, statistics, mathematics, or other quantitative field.
Candidates holding a degree in biological/medical science are also welcome to apply if they have extensive background in computational or statistical work.

Additional Qualifications    
Applicants must have substantial experience with statistical or epidemiologic data analysis. Preference will be given to candidates with demonstrated research interests in areas currently under investigation (e.g. asthma, COPD, lung biology, mucosal immunology) in the research group.

Experience and/or training in geospatial analysis, Bayesian disease mapping and other methods, spatial epidemiology
Strong quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with large datasets, particularly genome-wide assays such as RNA-seq

Knowledge of lung biology, mucosal immunology, biology of allergy, impact of environmental conditions, particularly poor-quality housing on respiratory health
Track record of, or strong potential for, independent funding

Special Instructions    
This position is funded by a T32 grant, which is limited to U.S. citizens, non-citizen U.S. nationals, and permanent residents.

Contact Information    
Midge Van Aller, Staff Assistant III
Harvard TH Chan School of Public Health
Department of Environmental Health
665 Huntington Avenue
Boston, MA 02115

Contact Email    ahaber@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    3 
Maximum Number of References Allowed    3