HMS - Associate in Biomedical Informatics (Patel Lab)

Position Description:

Chirag Patel's group in the Department of Biomedical Informatics, Harvard Medical School is seeking an Associate with a focus in Biomedical Informatics for gene-byexposome interactions and therapeutic discovery. Seeking a recent post-grad or MS level with a desire to research and learn more about biomedical research, multi-omic integration analytics and machine learning. In this role you will produce highly impactful biomedical informatics research that presents new innovations in methods and novel findings that inform disease etiology. The exposome is a promising and emerging modality to explain disease variation (papers in press in Nature Medicine) that impacts humans on all levels: from the single cell to populations, providing a rich substrate to do novel work. In this role you will gain experience developing approaches to research and analyze large multimodal datasets, with data ready to analyze you can focus on developing research findings resulting in publication.Responsibilities include:Research and translate exposome data science into real-world clinical applications Help to design exposome foundation models for disease physiology. Research and characterize how an individualʼs exposome drives variation in response to pharmaceutical intervention. Collaborate with interdisciplinary teams across Harvard (HSPH, HMS) and at partner universities to enhance research, data collection, cleaning, and integration processes, ensuring data accuracy and consistency. Produce high-impact peer-reviewed articles, and presentations for academic and non-academic audiences.

Basic Qualifications:

• BS, MS in Data Science, Statistics, Computer Science, Epidemiology, Environmental Health, or a related field.
• Demonstrated expertise in large scale analysis and familiarity with health-related datasets.

Additional Qualifications:

• Proficiency in statistical software (R, Python, etc.), and working knowledge of data management protocols.
• Experience with exposome or environmental data, multiomics integration, and exposure-wide association studies (ExWAS) and GIS preferred but not required. Learn with us!