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Postdoctoral Research Position in Computational Biology

Employer
Harvard University
Location
Cambridge, MA
Closing date
Aug 12, 2022

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Sector
Science, Life Sciences
Organization Type
Corporate
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Details

Title Postdoctoral Research Position in Computational Biology

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 University has openings for highly motivated postdocs to develop and apply novel computational approaches for next generation sequencing data analysis, particularly single-cell RNA sequencing, spatial transcriptomics, and related approaches.

Our research group uses single-cell genomics to study immunity at mucosal sites, with a focus on the lungs and particularly mechanisms of asthma. Past work has focused on discovering new cell types in the lungs (Nature, 2018), single-cell genomics of the small intestine (Nature, 2017), and using single-cell methods to describe how the mucosal immune system regulates epithelial stem cells (Cell, 2018). The successful candidate(s) will join an interdisciplinary team spanning several institutions, including the Harvard T.H. Chan School of Public Health, Brigham & Womens Hospital, and the Broad Institute of MIT and Harvard.

Projects in the lab aim to discover new aspects of lung physiology in health and disease using computational and systems biology approaches to analysis of single-cell RNA -sequencing data. These datasets derive from samples of human lungs, and also from mouse models of airway injury, inflammation and regeneration, and aim in particular to examine the impact of environmental exposures such as air pollution, and allergens on the lungs airways. The successful candidate will collaborate closely with clinical pulmonologists and immunologists to study the molecular mechanisms that underly airway tissue homeostasis and asthma pathogenesis. In addition, our group aims to develop new computational algorithms to examine cellular communication between the lungs and the local immune system, define pathways that are aberrant in disease, and discover new possible therapies. Fellows will be encouraged to take advantage of the rich and varied training and career development opportunities offered at HSPH .

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 strong background in computational or statistical work.

Additional Qualifications
  • Applicants must have substantial experience with data science and statistical 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.
  • Knowledge of lung biology, mucosal immunology, biology of allergy, impact of environmental conditions on respiratory health

    Special Instructions

For more information on the Department of Environmental Health, please visit us at: https://www.hsph.harvard.edu/environmental-health/

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 vanaller@hsph.harvard.edu

Equal Opportunity Employer

The Harvard T.H. Chan School of Public Health seeks to find, develop, promote, and retain the worlds best scholars. We are committed to upholding the values of diversity, equity, and inclusion in our school and the communities we serve. 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

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