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Post Doctoral Fellow in Deep Learning for the Microbiome - (3278418)

Description

Post Doctoral Fellow in Deep Learning for the Microbiome

The Gerber Lab (http://gerber.bwh.harvard.edu) is a multidisciplinary group at Brigham and Women’s Hospital/Harvard Medical School that develops novel computational 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. The director of the lab, Dr. Georg Gerber, MD, PhD, MPH, uses his unique expertise, combining statistical machine learning method development, medical microbiology, and human pathology, to leverage cutting-edge technologies to tackle scientifically and clinically important problems.

We are looking for an exceptional researcher who will play a major role in new initiatives in the lab to develop novel deep learning (DL) 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. Applications include forecasting microbial population dynamics in the gut for rational design of therapies, predicting the impact of the microbiome on the onset or progression of human diseases, predicting interactions with the host immune system, elucidating host-microbial metabolic interactions, and discovering functions of uncharacterized microbial metabolites and proteins.

Qualifications

The successful candidate will be highly motivated and creative, taking a lead role in developing new deep learning-based methods, analyzing data, and interpreting results. The position is a fantastic opportunity for an individual with either 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. Although some experience modeling biological or other complex systems is required, microbiome specific knowledge is not required.

Qualifications:

  • PhD in Computer Science, Computational Biology, Ecology, Mathematics, Physics, Statistics, or other highly quantitative discipline.
  • Excellent publication track record.
  • Strong mathematical background and skills (our emphasis is on developing new techniques, rather than applying off-the-shelf DL methods).
  • Some experience with deep learning methods (note that our lab’s primary focus is NOT imaging data, so experience with broader DL methodology is highly desirable).
  • Some experience modeling biological systems; microbiome experience is desirable, but not required.
  • Solid programming skills in Python, including PyTorch.
  • Superior communication skills and ability to work on multidisciplinary teams.

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 (MHMC) (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. With a recent grant from the Massachusetts Life Science center, the Division has built the Lab for AI/Deep Learning for the Microbiome, which has a state-of-the-art GPU cluster for model development, training and deployment. 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 and Boston Children’s Hospital.

To apply: email a single PDF including cover letter, CV, brief research statement and a 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). Incomplete applications will be considered non-responsive and unfortunately cannot be considered.

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.

EEO Statement 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.

Primary Location

: MA-Boston-BWH Longwood Medical Area

Work Locations

BWH Longwood Medical Area 

75 Francis Street 

Boston 02115

Job

: MD/PHD/Fellows/ PostDocs

Organization

: Brigham & Women's Hospital(BWH)

Schedule

: Full-time

Standard Hours: 40

Shift

: Day Job

Employee Status

: Regular

Recruiting Department: BWH Pathology

Job Posting

: Feb 16, 2024

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Confirmed 18 hours ago. Posted 30+ days ago.

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