Postdoctoral Fellow, Deep Learning for Microbiome – Gerber Lab

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.

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.
  • Experience with deep learning methods; experience developing DL models for biological sequencing data is highly desirable.
  • Experience modeling biological or other complex systems is required; microbiome experience is desirable, but not required.
  • Strong mathematical background with track record developing novel models and methods.
  • 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.

Graduate Students – Gerber Lab

I am always excited to work with talented graduate students with interests relevant to my lab, which focuses on developing novel machine learning/computational biology/wet lab 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.

In general, I can only be a primary advisor (and provide financial support) for students enrolled at Harvard or MIT. However, I am open to co-advising students at other institutions.

If you’re interested, email me at ggerber#bwh.harvard.edu. Please include your CV and a brief description of your research interests.

Students should have a high level of interest in:

  • Developing and applying new technologies to biomedical problems.
  • Advancing knowledge of the microbiome and its role in human health and disease.
  • Having your work make an impact on healthcare outcomes.
  • Working on an interdisciplinary team and collaborating with computational, wet lab and clinical scientists.

About the lab: the Gerber Lab 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 Bayesian statistical machine learning methods 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, which Dr. Gerber heads, at Brigham and Women’s Hospital (BWH) at Harvard Medical School (HMS), and the Massachusetts Host-Microbiome Center, 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.

Gerber Lab’s “MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics” is mSystems Editor’s Pick

Gerber Lab’s “MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics” is mSystems Editor’s Pick

Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. 

The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome

The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome

Through a $3.3M grant from the Massachusetts Life Science Center and in-kind support from Brigham and Women’s Hospital and Mass General Brigham, the BWH Massachusetts Host-Microbiome Center (MHMC) and Division of Computational Pathology will establish a new lab to develop and apply advanced AI/deep learning technologies to microbiome research. Dr. Georg Gerber, Chief of BWH Computational Pathology and co-director of the MHMC will head the new lab.

The microbiome is inherently complex and dynamic. Multi-omic data characterizing microbes in culture systems, animal models, and human populations can provide unique and complementary insights into these rich host-microbial ecosystems. However, to fully realize the potential of these data, sophisticated computational approaches are needed.

Artificial Intelligence (AI), and in particular Deep Learning (DL), are revolutionizing many fields, such as speech and image recognition. These technologies are also increasingly impacting the biomedical sciences.

The Lab aims to unleash the power of AI and DL technologies for the microbiome field.

Anchored by a dedicated large GPU with Tesla A100 nodes and CPU compute clusters, the Lab will develop custom AI/DL applications for the microbiome, deploy existing software in a managed and easy-to-use environment, and provide outreach and education to the microbiome community. The Lab will be staffed by principal investigators in the Division of Computational Pathology, as well as an application scientist and network engineers.

A joint initiative between the Brigham and Women’s Hospital (BWH) Division of Computational Pathology and the Massachusetts Host-Microbiome Center (MHMC), the Lab is funded by the Massachusetts Life Sciences Center and Brigham and Women’s Hospital/Mass General Brigham. Industry and academic users will be able to access the Lab through the MHMC’s existing core services model and through collaborations.

Gerber lab study showing gut metabolites predict C. diff recurrence

Gerber lab study showing gut metabolites predict C. diff recurrence

Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Using broad metabolomics data and statistics and machine learning models, Jen Dawkins, a HST PhD student and member of the Gerber lab, showed the metabolites in the gut can accurately predict C. difficile recurrence. These findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.

Dawkins JJ, Allegretti JR, Gibson TE, McClure E, Delaney M, Bry L, Gerber GK. Gut metabolites predict Clostridioides difficile recurrence. Microbiome. 2022 Jun 9;10(1):87. doi: 10.1186/s40168-022-01284-1. PMID: 35681218; PMCID: PMC9178838.