Applications are invited for a postdoctoral research position as part of an NIH-funded project focused on the spread and evolution of antibiotic resistance bacterial infections in hospitals. The project aims to develop and apply powerful new methods for resolving transmission dynamics and microbial evolution using computational models, electronic medical records, microbiological surveillance, and whole genome sequencing. The successful applicant will join a vigorous research team working on a range of problems in epidemiology, evolutionary medicine, and computational biology.

We seek applicants interested in developing cutting-edge inference methodology for individual-based models of pathogen transmission and antibiotic resistance evolution using data from a variety of sources. Applicants for this position should have a doctoral degree in Statistics, Bioinformatics, Epidemiology, Ecology, Applied Mathematics, Computer Science, or a related field. The successful applicant will have a record of scholarly publication and excellent written and oral communication skills.

The postdoctoral fellow will be supervised by a team composed of Profs. Robert Woods (Infectious Diseases, Computational Medicine and Bioinformatics, University of Michigan), Aaron King (Ecology & Evolutionary Biology, Complex Systems, Mathematics, Computational Medicine and Bioinformatics, University of Michigan), and Andrew Read (Center for Infectious Disease Dynamics, Pennsylvania State University). These researchers have long experience in the development, implementation, and application of novel methods to questions in infectious disease epidemiology, ecology, and evolution. The University of Michigan consistently ranks among the leading universities worldwide and has top-tier graduate programs in statistics, medicine, ecology & evolutionary biology, and epidemiology. Ann Arbor is also routinely rated one of the best places to live in the U.S. due to its affordability, lively culture, and natural beauty.

Compensation and start date. The salary is $52–64k; per year, depending on experience, and comes with the standard University of Michigan benefits package. Two years of funding are available, the second year contingent on adequate progress during the first. The start date is negotiable.

To apply: please submit a single PDF document containing (1) cover letter, mentioning which position is sought and the names and contact information of three references, (2) curriculum vitae, and (3) two representative papers, by email to robertwo@umich.edu and kingaa@umich.edu.

The University of Michigan is a Non-Discriminatory/ Affirmative Action Employer. Individuals from underrepresented groups are especially encouraged to apply.

Detailed project summary. Enterococcus faecium is a leading cause of hospital acquired infections, has proven refractory to infection prevention measures, and has evolved increasing levels of antibiotic resistance over the last 40 years. How resistance evolves and spreads in this pathogen is uncertain because transmission and selection are hidden processes: transmission occurs silently between asymptomatically colonized patients, which obscures the signal of selection observed in clinical isolates. The proposed work will develop and deploy powerful new statistical inference techniques to assimilate data from electronic medical records, microbiological samples, and whole genome sequences into explicit, mechanistic models of transmission and antibiotic resistance evolution in E. faecium. The work is made possible by unique features of the study system: we have documented ongoing transmission and resistance evolution in the pathogen E. faecium and possess both a nearly perfect record of patient movement and antibiotic exposure and a large collection of patient samples from a thorough and active surveillance protocol. The aim of the proposal is to develop and fit a detailed E. faecium transmission model to medical record data to precisely quantify transmission rates, recovery rates, the rate of evolution of resistance, drivers of these rates, including contact precautions and antibiotic exposure, and potential interactions between resistance and transmissibility. The methods developed herein will be applicable to a broad array of pathogens and clinical settings, and will facilitate the rational design of strategies to slow or even reverse the evolution of antibiotic resistance.