Description of Work Unit
In the Webb lab, we study how the interplay between ecological and evolutionary mechanisms affects the dynamics and persistence of ecological systems using a variety of quantitative techniques, including dynamical systems, simulation, Bayesian hierarchical modeling, and applied statistics. Examples of general questions we are currently addressing include:
What mechanisms predict the rates and patterns of disease transmission in livestock systems?
How do spatial processes affect disease spread?
How can data from multiple biological scales be combined to predict disease transmission?

Position Summary
We seek an outstanding individual to contribute to research in the lab of Dr. Colleen Webb at Colorado State University in collaboration with the USDA APHIS Center for Epidemiology and Animal Health (CEAH) with the potential to directly impact management of disease outbreaks in livestock settings. In this project, we use data from livestock systems and disease outbreaks to inform a model of livestock shipments at the U.S. national scale (the United States Animal Movement Model USAMM) and a model of livestock disease outbreaks (the United States Disease Outbreak Simulation USDOS). The Postdoctoral Fellow is needed to help transition these two models from their academic development to actual usage by the United States Department of Agriculture (USDA). This work is important to provide accurate risk assessments and emergency response preparedness for the livestock industry, and the management implications of the project bring project members into regular interaction with both government agencies and academia.
The core responsibilities of the position include working with USDA collaborators to identify a case study, likely with foot and mouth disease (FMD) in cattle, to structure transition of USAMM and USDOS for more general use by CEAH staff. In addition to the subject matter work on the FMD case study, the project will also involve developing a detailed transition plan including a pipeline for pre- and post-processing of model results appropriate for use by CEAH staff, developing training materials and documentation. The postdoc will produce high quality, peer-reviewed publications of results from the case study and also present results at conferences and to stakeholders. Additional opportunities include participating in projects to support risk analysis and risk identification with CEAH collaborators. This is a unique opportunity to develop models for direct application in disease management of livestock systems.
We look for candidates with strong quantitative skills, proven time and project-management skills, ability to work in a team environment and excellent communication skills.
The position is for one year with potential for renewal with satisfactory performance. Salary is commensurate with experience. For questions about this position, please contact Tanya Dewey at tdewey@rams.colostate.edu

Required Job Qualifications
Ph.D. by the time of job start in biology, ecology and evolution, statistics, or another appropriate field.
Significant experience with quantitative methods.

Preferred Job Qualifications
Experience with disease ecology, ecological modeling or advanced statistics.
Documented ability to bring projects to completion, especially as publications in peer-reviewed journals.
Evidence of intellectual leadership.
Excellent quantitative skills, proficiency in programming languages (e.g., R, C++, Matlab), and ability to communicate computational needs to information technology support staff.
Familiarity with diverse data analysis frameworks, especially spatio-temporal dynamic simulation models and/or Bayesian hierarchical models.
Experience working collaboratively in a group setting.
Experience working with non-governmental organizations, government agencies, or industry.
Excellent communication skills in English, both written and oral.

Diversity Statement
Reflecting departmental and institutional values, candidates are expected to have the ability to advance the Department’s commitment to diversity and inclusion.