Postdoctoral Researcher

Postdoctoral Fellow – Computational Genomics, AI, and Systems Pharmacology Harvard Medical School / Brigham and Women’s Hospital Division of Population Science and Network Medicine We are seeking an exceptional postdoctoral fellow interested in using genetics, multi-omics, machine learning, and network medicine to discover mechanisms and therapeutic targets for chronic lung diseases. The fellow will work with Dr. Matthew Moll (Assistant Professor) and Dr. Michael Cho (Associate Professor) at Harvard Medical School and Brigham and Women’s Hospital. Our group studies chronic respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), asthma, and pulmonary fibrosis, using large-scale human genetic and multi-omic datasets. This position is supported by an active NHLBI R01 focused on integrating human genetics, proteomics, single-cell data, and deep learning to identify biomarkers, biological mechanisms, and druggable targets for COPD exacerbations. Research We are looking for a postdoctoral fellow who wants to lead computational projects and translate large-scale human data into biological and therapeutic insights . Specifically, the fellow will lead projects integrating human genetics, proteomics, and AI to understand COPD exacerbations, identify predictive biomarkers and disease mechanisms, and use deep-learning and network approaches to discover cell-specific therapeutic targets and drug-repurposing opportunities. The fellow will have access to data from some of the largest respiratory and population cohorts in the world, including COPDGene, TOPMed, UK Biobank, All of Us, the Mass General Brigham Biobank, the Million Veteran Program, and the International COPD Genetics Consortium. Whole-genome genetic data Large-scale plasma and lung proteomics Bulk and single- single-nucleus RNA sequencing Epigenomic and metabolomic data Deep clinical phenotyping and longitudinal outcomes Novel cohorts of COPD patients with exacerbations Substantial preliminary work has already been completed, and several key datasets are cleaned and ready for analysis. The fellow will therefore be positioned to begin substantive scientific work immediately while also developing new directions. Who we are looking for We are looking for candidates who are intellectually curious, creative, and motivated to take ownership of ambitious computational projects. Strong candidates will be: Creative and intellectually curious: interested not simply in applying existing pipelines, but in asking new questions and developing better computational approaches when needed. Independent: able to take ownership of a scientific problem while knowing when to seek input and collaborate. Quantitatively strong: comfortable working with large, high-dimensional biological datasets. Collaborative: excited to work across genetics, computational biology, network science, statistics, pharmacology, and clinical medicine. Scientifically ambitious: motivated to lead manuscripts, present work nationally and internationally, and develop an independent research program. Experience with every method listed in this advertisement is not expected. Qualifications Applicants should have a PhD or equivalent doctoral degree in a quantitative or computational field such as: physics, computer science, machine learning/AI, bioinformatics, computational biology, statistical genetics, biostatistics, genetic epidemiology, applied mathematics, or a related discipline. Particularly valuable experience includes one or more of the following: Machine learning or deep learning (required) Network science / graph-based methods (required) Statistical or population genetics Proteomics or multi-omics integration Systems biology or systems pharmacology Strong programming skills in Python and/or R and evidence of reproducible computational research are expected. Candidates do not need prior expertise in COPD or pulmonary medicine. We are particularly interested in outstanding quantitative scientists who want to apply their expertise to important problems in human biology and medicine. Research environment and mentorship The fellow will join an interdisciplinary research environment at Brigham and Women’s Hospital and Harvard Medical School involving geneticists, genetic epidemiologists, biostatisticians, computational biologists, network scientists, clinicians, pharmacoepidemiologists, programmers, and computational engineers. The fellow will work closely with Drs. Moll and Cho, while collaborating with investigators across major national and international cohorts. Our goal is for the fellow to leave the position with a strong body of first-author work, expanded quantitative and biological expertise, and a clearly defined independent scientific direction. Funding is available immediately, with flexibility in the start date. BWH is an affirmative Action Employer. By embracing diverse skills, perspectives, and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Brigham & Women’s Hospital (BWH) strives to keep its workplace safe and secure for the protection of patients, visitors, and staff. Therefore, our offer of employment is contingent upon completion of the following items. Satisfactory reference checks Satisfactory criminal background checks Authorization to work in the United States Medical clearance from our Occupational Health department Annual compliance with Mass General Brigham’s Influenza Vaccination Policy Compliance with Mass General Brigham’s COVID 19 Vaccination Policy Successful review of the Medicare program's Sanctions and Exclusions List Verification of education, current licensures and registrations, and credentialing (if applicable) If you begin work before the results of these checks are determined, it is with full understanding that an unsatisfactory report may lead to termination. Reference information, including academic and/or employment records, final evaluations, and recommendations for future employment will be required. #J-18808-Ljbffr