The King lab at the University of Michigan and the Wale lab at Michigan State University are seeking a postdoctoral research fellow for a project focused on building and testing data-driven models of dynamics of pathogen burden, pathology, and immune activity in experimental infections.

The postdoctoral researcher will build on the PIs' previous work (Wale et al. PNAS 2019; doi:10.1073/pnas.1908147116, which used a novel modeling approach to quantify the contribution of pathogen and host immune activity to disease and revealed new immune defense strategies against malaria parasites. Specifically, the researcher will exploit rich datasets of high-resolution time-series data to extend existing models so as to increase their predictive power. The researcher will then use these models to design new experiments and analyze the resulting data with the goal of revealing the rules that govern the dynamic deployment of the immune system and, ultimately, infection dynamics.

The postdoctoral researcher will be co-advised by Drs. Aaron King (Ecology & Evolutionary Biology, Complex Systems, Data Science, Computational Medicine & Bioinformatics, University of Michigan) and Nina Wale (Microbiology & Molecular Genetics, Integrative Biology, Ecology, Evolution & Behavior, Michigan State University). Dr. King is a leader in the development of powerful computational methods for scientific inference. Dr. Wale exploits model systems in new ways to obtain pioneering insights into the ecological and evolutionary dynamics of infections, including the mouse model of malaria which is the focus of this project. As part of this collaboration, the postdoctoral researcher will have access to a broad range of expertise, resources, and mentoring, in fields from microbiology and immunology to computational biology.

Qualifications. We seek a highly-motivated, creative scientist driven to discover the principles that underlie the dynamics of infection and immunity. Applicants should be excited at the prospect of working in an interdisciplinary team of experimental and computational biologists, have exceptional written and oral communication skills, and have a PhD in Systems Biology, Applied Mathematics, Statistics, Computer Science, Computational Ecology & Evolutionary Biology, or a related discipline.

Compensation and start date. The salary depends on experience, according to the standard NIH schedule, and comes with the standard University of Michigan benefits package. The start date is negotiable, with a target of January 2021.

This is a one year term-limited position with a possibility of renewal. Although the position will be jointly supervised by Drs. King and Wale, the position will be formally situated at the University of Michigan in Ann Arbor. In general, such Postdoctoral Research Fellow appointments are renewable annually for a maximum of three years. Additional information regarding University of Michigan Postdoctoral Research Fellow appointments can be found here.

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

How to apply. Please submit a single PDF document containing (1) cover letter including the names and contact information of three references, (2) curriculum vitae, (3) two representative papers, by email to kingaa@umich.edu and walenina@msu.edu. Please use the subject line "POSTDOC APPLICATION".

Applications are invited for a postdoctoral research position working on a new NSF/NIH-funded project at the interface of statistics, modeling, and epidemiology aiming to develop powerful new methods for spatiotemporal dynamics and to combat dengue fever. Specifically, the postdoc will be part of an interdisciplinary team working to develop statistical methodology for partially observed spatiotemporal dynamic systems and to use this methodology to infer dynamic models that explain and predict patterns of dengue incidence in Rio de Janeiro.

The postdoctoral fellow will be jointly supervised by Profs. Edward L. Ionides (Statistics) and Aaron A. King (Ecology & Evolutionary Biology, Complex Systems, Mathematics) at the University of Michigan. The project team also includes Prof. Mercedes Pascual of the University of Chicago. These researchers have long experience in methods development, implementation, and application in infectious disease epidemiology and ecology. The University of Michigan consistently ranks among the leading universities worldwide and has top-tier graduate programs in statistics, 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.

Applicants should have a doctoral degree in Statistics, Epidemiology, Ecology, Applied Mathematics, Computer Science, or a related field, a good record of scholarly publication, and excellent written and oral communication skills.

Compensation and start date. The salary is $53k per year and comes with the standard University of Michigan benefits package. Two years of funding are available, contingent on adequate progress during the first year. The start date is negotiable, with a target of July 2019.

To apply: please submit a single PDF document containing (1) cover letter including the names and contact information of three references, (2) curriculum vitae, (3) two representative papers, by email to ionides@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. Statistical analysis of partially-observed, nonlinear, stochastic spatiotemporal systems is a methodological challenge. Existing inference algorithms suffer from a "curse of dimensionality" that prohibits their applicability to models describing interacting dynamic processes occurring within and between many spatial locations. In this project, new inference algorithms will be developed, and shown in theory and in practice to advance capabilities for spatiotemporal data analysis. Methodological research will be carried out in the context of addressing transmission of dengue virus within a tropical megacity. Global incidence of dengue has risen 30-fold over the past fifty years, with notable geographical expansion in South and Central America. The municipality of Rio de Janeiro is a focal point for dengue transmission in this region. Spatiotemporal data on dengue cases in Rio de Janeiro will be analyzed, together with data on human movement, temperature, and rainfall. Policy decisions for the detection, control, and potential eradication of infectious diseases are best informed by model-based understanding of disease transmission. Improved understanding of the spatiotemporal dynamics of disease transmission will have implications for improvements in disease control. Mathematical models will be developed to describe spatiotemporal dynamics of dengue transmission, and the novel statistical methodology will be used to link these models to the data from Rio de Janeiro.

Applications are invited for a postdoctoral research position as part of a new NIH-funded project at the interface of epidemiology, evolutionary dynamics, and computational modeling and 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.

We seek applicants interested in developing novel bioinformatics methods to resolve patterns of transmission and antibiotic resistance evolution using whole genome sequencing data. Applicants for this position should have a doctoral degree in Bioinformatics, Evolutionary Biology, Computer Science, or a related field. The successful applicant will have a good 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 $51k per year 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, with a target of July 2019.

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.

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.