Postdoc position — machine learning for bacterial mutation calling

The lab: The Lieberman Lab studies real-time evolution within the microbiomes of individual people. We aim to address fundamental questions about the interactions of evolution and ecology in microbial communities, while building the foundational ecological knowledge needed to realize the promise of precision microbiome engineering.

The project: High-specificity variant calling is required for precision evolutionary analysis (e.g. use a phylogeny to infer the directionality of bacterial transmission between family members). Current approaches for detecting genetic mutations and variations only have the needed fidelity on very closely related reference genomes. Moreover, filters for removing false-positive variants must be tuned for each data set and reference genome, or manually inspected sample-by-sample and position-by-position for fidelity. This project will harness our large collection of diverse microbial genomes with validated variant calls, our in-house toolboxes for investigating potential variants, and the power of machine learning to develop an automated pipeline for high-specificity detection of polymorphisms in bacterial populations that is agnostic to reference genome choice. We believe that real progress can and must be made in this space, as the size of typical data sets continues to grow. There are opportunities for extension of this project to metagenomic data and/or investigation of unique data sets of skin microbes. This position is fully funded by the NIH’s New Innovator Award to Professor Lieberman.

The ideal candidate: Has extensive experience in some (but not necessarily all) of the following areas: Microbial genomics, Evolutionary reconstruction, Machine learning, Open-source software development. Has advanced expertise in Python, and experience with workflow manager such as Snakemake.

To apply: Send an email to referencing this job post. Include a CV, a manuscript (preprints are great), and have 2 recommenders send letters of support.

We are is committed to creating a healthy work environment where individuals from all backgrounds can thrive personally and professionally. We pay above the NIH postdoc minimum.

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