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Post-doctoral Fellow in Machine Learning/Computational Biology at Harvard Medical School

Employer
Partners Healthcare System
Location
South Boston, Massachusetts, US
Salary
Competitive
Closing date
Dec 18, 2021

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Sector
Academic / Research
Field
Conservation science
Discipline
Statistics, Biology
Salary Type
Salary
Employment Type
Fellowship
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Post-doctoral position available (with flexible start dates) to develop novel machine learning/computational biology approaches to further understanding of the microbiome--the trillions of microbes living on and within us. This fascinating, complex and dynamic ecosystem is crucial for human health, and when disrupted may contribute to a variety of diseases including infections, arthritis, allergies, cancer, heart and bowel disorders. Over the past decade, sequencing and other high-throughput methods have provided data about the microbiome at unprecedented scale. We are looking for talented and highly motivated post-docs with strong mathematical backgrounds (computer science, computational biology, statistics, mathematics, ecology, physics, etc.) who want to develop and apply novel machine learning methods that will further understanding of the microbiome. Areas of interest include: * Deep learning models using microbiome sequence information (i.e., for embedding or supervised tasks) * Fully-differentiable interpretable models based on relaxations (e.g., rule-based) * Nonparametric Bayesian models * Deep dynamical systems models The position could be a good fit for either someone with a strong machine learning background who wants to get domain-specific research experience, OR someone with a strong mathematical background who wants to get more machine learning experience. The candidate is expected to engage with the broader machine learning and computational biology communities by presenting work at top conferences, as well as publishing applications of new methods in high impact journals. Although some experience modeling biological or other complex systems is required, microbiome specific knowledge is not required. About the lab: the Gerber Lab (http://gerber.bwh.harvard.edu) develops novel statistical machine learning models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applies these findings to develop new diagnostic tests and therapies. A particular focus of the Gerber Lab is understanding dynamic behaviors of host-microbial ecosystems. Our work in this area includes the fully Bayesian MCTIMME, MDSINE and MITRE algorithms, for discovering temporal patterns in microbiome data, inferring dynamical systems models from microbiome time-series data, or predicting host status from microbiome time-series data with human interpretable rules. We have applied these methods to a number of clinically relevant questions including understanding dynamic effects of antibiotics, infections and dietary changes on the microbiome, and designing bacteriotherapies for C. difficile infection and food allergy. We also apply our methods to synthetic biology problems, to engineer consortia of bacteria for diagnostic and therapeutic purposes. Environment: the Gerber Lab is located in the Division of Computational Pathology (http://comp-path.bwh.harvard.edu), which Dr. Gerber heads, at Brigham and Women's Hospital (BWH) at Harvard Medical School (HMS), and the Massachusetts Host-Microbiome Center (http://metagenomics.partners.org), which Dr. Gerber co-directs. BWH, an HMS affiliated teaching hospital is adjacent to the HMS main quad and is the second largest non-university recipient of NIH research funding. The broad mandate of the BWH Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40 established investigators, 50 postdoctoral research fellows, and 100 research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes HMS, Harvard School of Public Health, Boston Children's Hospital and the Dana Farber Cancer Institute. Qualifications: * PhD in computer science, computational biology, ecology, mathematics, physics, statistics, or other quantitative discipline * Excellent publication track record * Strong mathematical background with track record developing novel models and methods * Solid programming skills in Python; this isn't a software engineering job, but you will need to be able to develop efficient implementations and apply your work to real biological data * Experience modeling biological or other complex systems required; microbiome experience desirable, but not required * Superior communication skills and ability to work on multidisciplinary teams Email single PDF including cover letter, CV, unofficial transcripts, brief research statement 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.)

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