Kristian Andersen, Scripps Institute, California
We are interested in the interaction between the human host and highly pathogenic viruses such as Ebola and Lassa. Our goal is to understand how these viruses evolve in response to selection pressures imposed by the host immune system and to use that knowledge to inform better vaccines and therapeutics. We use a combination of computational biology, experimentation, and fieldwork in West Africa.
Andrés Colubri, University of Massachusetts Chan Medical School, Massachusetts
The Colubri Lab (or CoLabo) brings together computational scientists, software developers, and visual designers to create new methods and tools for epidemiological modeling, digital health, visualization of biomedical data, and STEM education. We are particularly interested in applying participatory outbreak simulations with mobile phones (https://operationoutbreak.org/) to improve preparation and response to future pandemics. We collaborate with groups from different fields to create a multidisciplinary vision of science, technology, and art.
Mireille Kamariza, University of California, Los Angeles, California
The Kamariza Lab conducts research at the intersection of chemistry, biology, computational and molecular engineering to unlock new possibilities for research, diagnostics, therapeutics, and precision medicine in resource constrained environments. In all projects, we work closely with our global partners to evaluate, optimize, and deploy innovative technologies from our lab directly into the field.
Yana Kamberov, University of Pennsylvania, Pennsylvania
We are developmental and evolutionary biologists investigating how adaptive variation has impacted humans at a genetic and phenotypic level. To address this, we bridge evolutionary genomics with developmental discovery and functional testing in mice. Our mission is to not only gain a comprehensive understanding of the mechanisms and consequences by which evolution has shaped our species, but also to leverage these findings towards the betterment of human health.
Elinor Karlsson, UMass Medical School, Massachusetts
In the Karlsson lab, we use signals of ancient selection as a tool for investigating how the genome works. We are developing new methods for identifying the genetic variants and regulatory pathways that underlie complex, polygenic diseases, with the goal of translating these discoveries into advances in health care. We have two main areas of research: infectious disease resistance in humans and behavioral genetics in dogs. We are looking for postdocs and graduate students interested in developing and applying creative new computational approaches for investigating large genomic datasets.
Jacob Lemieux, Massachusetts General Hospital, Massachusetts
The Lemieux lab integrates computational and experimental studies of microbial genetic variation to understand how pathogens spread in the population and cause disease in human hosts. Our studies focus on Borrelia burgdorferi, the bacterial agent of Lyme disease, and SARS-CoV-2, the cause of COVID-19. Major projects in the lab include the use of computational screens and genome-wide association studies to identify microbial genes associated with disease phenotypes, for both Lyme disease and COVID-19, followed by the use of reverse genetic tools to characterize mechanisms of disease-associated loci in experimental systems. Candidates with wet-lab, dry-lab, and hybrid experience and interests are welcome. Interested candidates should reach out directly to Dr. Jacob Lemieux, Assistant Professor of Medicine at Harvard Medical School and Massachusetts General Hospital at jelemieux@partners.org, with a CV and cover letter describing their interest in the lab and previous scientific work.
Cameron Myhrvold, Princeton University, New Jersey
The Myhrvold lab develops CRISPR-based technologies for studying viral and host RNA, with an emphasis on technologies that will allow us to better understand biological processes such as transcriptional regulation and RNA localization. We also apply these technologies to detect and destroy viral RNAs, with a focus on SARS-CoV-2, the causative agent of COVID-19.
Anne Piantadosi, Emory University, Georgia
The dynamic, growing translational research laboratory at the Emory University School of Medicine is looking for a postdoc with expertise in computational biology to join us in studying the genomics of emerging viruses, including SARS-CoV-2. The lab would be happy to hear from exceptional applicants who may not meet the exact job description (e.g. individuals looking for a combination of wet/dry lab work, or looking for a research scientist position). They will also consider applicants who want to work remotely from their current place of residence. Interested candidates should apply using this link and can also reach out to Anne directly with a cover letter, CV, and contact information for 3 references.
Steve Reilly, Yale University, Connecticut
The Reilly lab is broadly interested in how variation in human genomes leads to the diverse array of phenotypes observed across evolution, diseases, and traits. We’re a multi-disciplinary team with a variety of backgrounds including genomics, math, biochemistry, machine-learning, and population genetics working to build new high-throughput tools for genome interpretation. To achieve such large-scale science, we’re part of many international consortia, including ENCODE, IGVF, and Zoonomia, in addition to working closely with the Sabeti Lab. Our foundational goal is to answer one of the remaining fundamental questions in biology: “how do genetic changes lead to functional changes at the molecular, cellular, and phenotypic level?”
Katherine Siddle, Brown University, Rhode Island
We are interested in understanding the influence of genetic variation on the severity and outcome of infection, and in leveraging genetic variation to improve surveillance and diagnosis of infectious diseases. We approach this problem through the lens of evolution, and the role that selective pressures imposed by the host and pathogen on each other play in shaping past, current and future diversity. To tackle these questions, we apply a combination of computational and experimental approaches in close collaboration with researchers, clinicians, and public health experts in the US and overseas. We are actively seeking motivated individuals to join our growing team – please contact Katie directly for more information.
Jesse Shapiro, McGill University, Canada
The broad theme of the Shapiro lab is microbial evolution and ecology on “human” time scales (that is to say, relatively short time scales of years, months or days). The lab uses a combination of genomic and metagenomic sequencing and computational biology to understand fundamental processes of natural selection, diversification and speciation in bacterial populations. Current research projects in the lab include:
1. Genomic analysis of cholera transmission and microevolution (with collaborators at MGH, Boston and ICDDR, Bangladesh; funded by the Canadian Institutes for Health Research)
2. Evolutionary dynamics and population genomics of cyanobacterial blooms (with collaborators at the National Research Council of Canada and UFMG, Brazil; funded by the Natural Sciences and Engineering Research Council of Canada)
3. Interactions between diet, mercury toxicity and the microbiome in Inuit populations of Northern Canada
Ryan Tewhey, Jackson Laboratory, Maine
The Tewhey Lab is focused on understanding the precise genetic architecture of complex traits and disease risk in humans. Using both experimental and computational approaches we engineer novel genomic technologies to better characterize non-coding regulatory regions and learn how natural DNA variation impacts gene expression. We maintain close ties with the Sabeti Lab through our shared work as part of the ENCODE consortium. Our long-term goal is to improve the understanding of regulatory grammar in both human and mouse systems in order to build animal models that better reflect human risk to disease.
Focusing on the natural variations of human physical traits, the Wang Lab is interested in knowing: 1) as human beings, how different we are – at the individual level and at the population level; 2) the genetic and environmental factors responsible for the natural variation of human physical traits; 3) how the natural variation of human physical traits is related to the health status. We approach our research goal from a data-driven angle – by leveraging available data resources from large-scale cohorts, and by developing and integrating the state-of-the-art computational genomics and machine learning approaches.
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