The Microbiome AI/Deep Learning Lab in the Massachusetts Host-Microbiome Center and Division of Computational Pathology at Brigham and Women’s Hospital/Harvard Medical School is seeking a scientist with experience in machine learning. You will develop, deploy, and apply machine learning approaches, with a special emphasis on deep learning, to a variety of microbiology data sources. Applications will include forecasting microbial population dynamics in the gut, predicting impact of the microbiome on host phenotype, tracking infections in human populations, elucidating microbial metabolism, and discovering functions of uncharacterized microbial metabolites and proteins. An important component of the position will also include engagement with the broader research community to identify new application areas.
Applicants should have a high level of interest in:
- Applying new deep learning technologies to biomedical problems.
- Advancing knowledge of the microbiome and its role in human health and disease.
- Having your work make a direct impact on healthcare outcomes.
- Working on an interdisciplinary team and collaborating with computational, wet lab and clinical scientists.
- Engaging with the broader research community to advance applications of AI/deep learning for the microbiome.
About the environment: The Microbiome AI/Deep Learning Lab is a newly established initiative within the Massachusetts Host-Microbiome Center (MHMC) and the Division of Computational Pathology (DCP) at Brigham and Women’s Hospital (BWH)/Harvard Medical School (HMS). With recent funding from the Massachusetts Life Sciences Center, the Lab is building a state-of-the-art compute cluster with extensive GPU and CPU nodes, with the objective of making advanced deep learning technologies broadly available to microbiome researchers. The MHMC is a research and core facility that has worked with 100+ groups in the US and internationally to promote understanding of host-microbiome interactions in health and disease, emphasizing a focus on function to define causative effects of the microbiota and to harness this knowledge in developing new therapies, diagnostics and further commercial applications. The DCP is a research division with a broad mandate to develop and apply advanced computational methods for furthering the understanding, diagnosis and treatment of human diseases. BWH is an HMS affiliated teaching hospital, adjacent to the HMS main quad, and the second largest non-university recipient of NIH research funding.
- PhD in Computational Biology, Computer Science, Physics, Statistics, Quantitative Microbial Genetics, Quantitative Ecology, or related quantitative discipline, with demonstrated experience in machine learning.
- Strong publication track record.
- Expertise with Python and machine learning and deep learning libraries such as PyTorch.
- Experience with bioinformatics methods and common pipelines for next generation sequencing data analysis.
- Experience with Unix, shell scripting, and high-performance computing environments (e.g., SLURM/LSF).
- Experience with organizing and managing large multi-omics datasets.
- Experience with microbiology/microbiome applications and metabolic modeling tools would be a plus.
- Strong written and oral communication skills.
Email single PDF including cover letter, CV, and list of at least three references to Dr. Georg Gerber (ggerber#bwh.harvard.edu). In your CV, indicate whether you are a U.S. citizen/permanent resident or visa holder (and list visa type).
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.