Description
The Gao Lab at Harvard T.H. Chan School of Public Health is seeking an outstanding postdoctoral research fellow to develop and apply high-resolution mass spectrometry (HRMS)-based exposomics and metabolomics to uncover environmental and molecular determinants of complex lung disease etiology (e.g., lung cancer, asthma, and other environmentally influenced respiratory outcomes). Our lab integrates cutting-edge analytical chemistry, exposure science, and data science to advance precision environmental health and mechanistic understanding of environmentally induced lung diseases.
Research Focus and Opportunities
The successful candidate will lead and contribute to projects that leverage LC/GC-HRMS exposomics and metabolomics in human biospecimens and/or environmental samples, with emphasis on identifying exposures, metabolic signatures, and pathways linked to lung disease risk and progression. The position offers opportunities to work with rich epidemiologic and clinical resources and to publish in high-impact journals in environmental health, omics, and pulmonary medicine.
Potential project directions include, but are not limited to:
Untargeted and targeted chemical exposomics in longitudinal/prospective cohorts to discover risk/protective factors for lung disease
Integrated exposomics–metabolomics analyses to identify biologic pathways and biomarkers relevant to lung disease etiology
Methods development for HRMS data processing, chemical annotation/identification, and exposome-wide association studies
Multi-omics and exposure mixture modeling; reproducible workflows and open, scalable analysis pipelines
Responsibilities
Perform and/or oversee HRMS-based data generation (LC/GC-HRMS and targeted validation as appropriate) and quality control
Conduct computational analysis of HRMS datasets, including non-targeted feature processing, annotation, and statistical modeling
Integrate multi-omics results with epidemiologic/clinical data; contribute to manuscripts, conference presentations, and grant-related activities
Collaborate with interdisciplinary teams across environmental health, biostatistics, epidemiology, and pulmonary research communities
Requirements
Required Qualifications
Ph.D. (or equivalent doctoral degree) in analytical chemistry, environmental health, epidemiology, bioinformatics/biostatistics, cheminformatics, or a related field
Demonstrated experience with the analysis of HRMS datasets
Programming skills in R and/or Python
Hands-on experience with LC/GC-HRMS operation and/or method development
Excellent written and verbal communication skills and ability to work independently and collaboratively
Preferred Qualifications
Experience analyzing large-scale omics datasets and integrating multi-omics or exposure data with clinical/epidemiologic outcomes
Familiarity with exposure science, toxicology, and/or respiratory disease research
Appointment, Compensation, and Benefits
This position is supported by an NIH T32 training grant. Per NIH policy, appointment is limited to U.S. citizens, U.S. non-citizen nationals, or U.S. permanent residents at the time of appointment.
This is a full-time postdoctoral position with an initial appointment of one year, with the annual renewal based on performance and funding availability. Salary and benefits are competitive and aligned with institutional and NIH/NRSA guidelines.
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