Job Description

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.

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 ([email protected]). In your CV, indicate whether you are a U.S. citizen/permanent resident or visa holder (and list visa type).

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 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 (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.

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.