Computational Biology (ML4Bio) Postdoctoral Fellow – Gibson Lab

Opening for a Postdoctoral Research Fellow to join the Gibson Lab at Harvard Medical School and Brigham and Women’s Hospital. We leverage tools from machine learning and control theory to understand biological systems. Control theoretic concepts are integrated both in the design of our optimization schemes and statistical machine learning models, as well as in the design of our in vitro and in vivo experiments. Our main area of focus is the microbiome and microbial dynamics more specifically. Applications include the design of bacteriotherapies (bugs-as-drugs), developing methods to learn microbial dynamics at ecosystem-scale, studying the impact of phages on microbial communities, methods for tracking low abundance pathogens, and methods for integrating multiple data modalities and prior knowledge (from other studies or databases) in time-series models. We focus on Bayesian methods that propagate measurement uncertainty throughout the model so that we can access confidence in model parameters and to help prioritize follow-up experiments. ML techniques applied include variational inference, Bayesian non-parametric models, and relaxation techniques (for making discrete models differentiable).

The specific project(s) you will be working on will fall under one of the following grants. Follow the links below for more details:

The candidate will also be encouraged to design their own experiments as well, which would then be carried out by staff in the germ-free mouse facility or in a collaborating wet lab. In addition to addressing our biological questions we also include key experimental components that can aid in validating our methods that would otherwise not be included in a purely hypothesis driven experiment. For a candidate wanting some wet lab experience we are looking to develop new single-molecule enzyme-linked immunosorbent assays (digital ELISA) to measure low abundance host inflammatory markers in feces (please mention this explicitly in your cover letter if interested).


  • PhD in computer science, applied mathematics, ecology, computational biology, systems biology, 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 biomedical data
  • Ability to reside in the U.S. and legally work in the country.

About the lab environment

The Gibson Lab is located in the Division of Computational Pathology at Brigham and Women’s Hospital (BWH), a Harvard Medical School teaching hospital, which is the second largest non-university recipient of NIH research funding. The broad mandate of the 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 the Harvard Medical School quad, Harvard School of Public Health, Boston Children’s Hospital, and the Dana Farber Cancer Institute. Many of our lab members also have appointments at the Massachusetts Institute of Technology and the Broad Institute.

Applications Process

Submit: (1) brief research statement (not to exceed 2 pages); (2) curriculum vitae; (3) two most relevant publications; (4) names and contact information of three individuals who can serve as references to: Travis Gibson, If you wish to chat briefly over Zoom before submitting materials to learn more details about our ongoing work, please inquire about this possibility.

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.

Gibson Lab receives $2.2 Million NIH R35 grant “Machine Learning and Control Principles for Computational Biology “

Gibson Lab receives $2.2 Million NIH R35 grant “Machine Learning and Control Principles for Computational Biology “

Grant Abstract: With our increasing ability to measure biological data at scale and the digitalization of health records, computational thinking is becoming ever more important in the biological science and healthcare. The research directions proposed in this grant look to build robust machine learning models and tools for computational biology by including principles and analysis from other engineering fields, like control, that have a proven record of incorporating robustness into the systems they have automated. This increased robustness will save resources during the development of these machine learning models. It will also lead to more reliable diagnostics, clinical tools, and machine learning based biological discoveries. We have proposed three future research directions at the intersection of machine learning, control, and computational biology (a) modeling dynamical systems, (b) robust optimization schemes (c) control principles for in vivo modeling of microbial communities. The first proposed research area involves the development of flexible models for performing inference on dynamical systems models with time-series data. Dynamical systems models are able to learn mathematically causal relationships between variables, compared to other models whose parameters may only have correlative relationships. Our flexible models will be differentiable allowing them to be trained using the same efficient algorithms and hardware that have propelled deep learning models into the spotlight. These differentiable methods will allow for us to more easily integrate the uncertainty associated with biological measurements into our models. The second research area looks to develop more robust gradient optimization algorithms, the work horse for training deep neural networks. Many of the popular algorithms used to train deep neural networks were not explicitly designed to be robust. By developing more robust optimization techniques machine learning models trained on disparate data sets at different hospitals or labs will be more reproducible and will require less time for tuning parameters, ultimately saving resources as well. These robust optimization techniques will also aid in the certification of machine learning based tools that will ultimately be deployed in the clinic. The third research area we propose is an approach for the discovery and design of robust microbial communities. Communities of commensal, or engineered, bacteria have long been proposed as alternative therapies for the treatment of gut related illness (“bugs as drugs”). We propose a top down approach to identifying putative microbial consortia members from time-series experiments with germ free mice colonized by complex flora. By beginning the consortia design process in vivo we hope to overcome the challenge that many other attempts at consortia construction have encountered where in vitro designed communities do not reproduce their intended properties once transferred into living host organisms. The tools from this work will be built using open access software and all data will be made easily accessible and explorable to the public.

Link to NIH AwardGibson Lab Website

Gibson Lab receives $450K NIH R21 grant “Tracking the microbiome: purpose-built machine learning tools for tracking microbial strains over time”

Gibson Lab receives $450K NIH R21 grant “Tracking the microbiome: purpose-built machine learning tools for tracking microbial strains over time”

Grant Abstract: Approximately 150 million people annually experience urinary tract infections (UTI), the most common cause of which is uropathogenic Escherichia coli (UPEC). The gut is a known reservoir of UPEC, which typically reside at low abundance, but can transcend the periurethral area to invade the bladder. While the E. coli population within the gut can be diverse, it has been suggested that certain strains have a greater propensity to migrate and cause infection. This may be one driving factor to explain why half of those with an acute infection have a recurrence even after taking antibiotics that clear the first infection from the urinary tract. Being able to detect and track E. coli strains over time would have direct clinical applications for those patients who have frequent recurrences due to gut UPEC carriage. One such clinical application would be early detection and intervention before the onset of infection. Unfortunately, current metagenomic algorithms are not capable of performing strain tracking accurately enough for clinical relevance, especially for low abundance species such as E. coli. A major factor for this lack of accuracy is that all current state-of-the-art metagenomic tools completely ignore temporal dependence between samples. Even if it is known that multiple samples are from the same patient, current tools analyze those samples as if they were independent. Furthermore, many metagenomic tools ignore the sequence quality information that is provided for every nucleobase in every read. We propose to develop a more precise strain tracking algorithm that does take this additional information into account, making the tool host-time-quality aware. Finally, we will pilot and validate our algorithm on a clinically relevant gnotobiotic colonization model. Specifically, humanized germ-free mice will be undergoing two rounds of E. coli challenges with therapeutic perturbations from antibiotics or mannosides, a small molecule precision antibiotic-sparing therapeutic. We propose the following specific aims: (1) Develop the first purpose-built computational method for tracking bacterial strains in the microbiome over time, (2) Gnotobiotic mouse model undergoing UPEC challenges and a therapeutic perturbation. These aims would advance the microbiome field forward allowing for the future development of therapeutics and clinical diagnostics.

Link to NIH AwardGibson Lab Website

$2.9M grant from the National Science Foundation  “The rules of microbiota colonization of the mammalian gut”

$2.9M grant from the National Science Foundation “The rules of microbiota colonization of the mammalian gut”

The Gerber lab in collaboration with the Wang lab at Columbia and the Gibson Lab at BWH have received a $2.9M grant from the National Science Foundation to develop and apply novel computational and experimental methods to elucidate fundamental rules governing the formation and maintenance of complex microbial ecosystems in the mammalian gut.

Abstract: Microbiomes, or the collections of trillions of bacteria and other micro-organisms living on, within and around us, have enormous impact on human life. For example, they help people digest food, promote the growth of farm animals and crops, and degrade pollutants in the environment. Despite the importance of microbiomes, the processes governing their formation and maintenance remain poorly understood. The mammalian gut is a particularly intriguing system for microbiome studies, since a diverse collection of microbes has evolved that specifically colonizes and functions in that environment. The goal of the project is to derive fundamental rules that describe and predict the dynamic process of microbial colonization of the mammalian gut. To achieve this goal, the team of investigators will develop new computer-based methods to automatically extract predictive and explanatory rules from large microbiome data sets. The team will also develop new experimental tools and generate data sets in mouse measuring how microbiomes change over time and across space in the mammalian gut. Overall, the project will further the understanding of the formation of microbiomes in mammals and can provide broader insights into the emergence of other microbial ecosystems, such as those in soil and marine environments. These insights could ultimately help scientists to rationally alter or maintain microbiomes in different environments to benefit human activities. The project will also generate practical resources for the scientific community (computer-based tools and datasets) and provide education on the microbiome to college and elementary school students through courses and hands-on labs.

A wealth of genomic data provides information as to which microbes are present in environments, but little insight into underlying factors that explain or predict complex assemblages of microbial consortia. This project aims to elucidate mechanistic factors that drive the dynamic process of microbial colonization of the mammalian gut. These determinants will be investigated at multiple systems scales, from the level of microbial communities down to the level of individual genes. The project will leverage high-throughput experimental methods developed by the investigators, to generate data characterizing functional genetic selection and spatial organization of microbiota in the mammalian gut. From the Computer Science perspective, the project will develop new computational methods to infer human-interpretable rules and other structured outputs from complex and noisy high-throughput microbiome datasets, using Bayesian and neural-style approaches that incorporate prior biological knowledge while scaling to massive datasets. This project has three main thrusts: 1) Learn microbial community-level rules that quantitatively predict population dynamics of mouse gut colonization and assess these rules across differing ranges of microbial diversity and composition, 2) Elucidate microbial gene-level mechanisms that predict mouse gut colonization dynamics, and 3) Profile microbial spatiotemporal organization and dynamics during gut colonization at the species and gene level to predict microbial community dynamics. The project is expected to establish a set of new computational and experimental tools and principles for understanding the rules of microbial colonization of the gut, with potential applications to other ecosystems including gut microbiota of non-mammalian species as well as complex environmental microbiota.