Graduate Students

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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.

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Postdoctoral Fellow, Deep Learning for Microbiome

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Post-doctoral positions available (with flexible start dates) to develop novel deep learning 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 deep learning methods that will further understanding of the microbiome.  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. From the machine learning perspective, areas of interest include:

  • Fully-differentiable interpretable probabilistic models based on relaxations and variational inference
  • Deep Bayesian, dynamical systems and other structured models
  • Neural topic models
  • Deep learning models using sequence information

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.

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 an impact on healthcare outcomes.
  • Working on an interdisciplinary team and collaborating with computational, wet lab and clinical scientists.

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 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.

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, with PyTorch experience desirable.
  • 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).

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.

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Research Scientist, Machine Learning for Microbiome

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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.

Required Qualifications:

  • 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.
  • Programming experience in Python.
  • Experience with Unix, shell scripting, and high-performance computing environments (e.g., SLURM/LSF).
  • Experience with bioinformatics methods and pipelines for next generation sequencing data analysis.
  • Experience with organizing and managing large multi-omics datasets.
  • Strong written and oral communication skills.

Desired Qualifications:

  • Experience with PyTorch.
  • Experience with microbiology/microbiome applications and metabolic modeling tools.

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.

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