We are searching for a postdoctoral research scholar skilled in infectious disease modeling to join our team immediately. This position will be primarily involved with the development of a new computational model for analyzing the spread of diseases among food-animal population. The modeling work will focus on developing models primarily centered on four thematic pillars:

1. Social network analysis of cattle, small ruminant, and swine to guide intelligent surveillance.
2. Based on legal movement and animal trafficking information (available from the local government in Brazil), reconstruct and investigate contact networks and evaluate routes used for the transit of illegal movements in areas with cattle trafficking activities, in the border with Brazil, Argentina, and Uruguay.
3. Mathematical modelling. Through the development of transmission models, to investigate the impact of control strategies. Under the introduction scenario of Foreign Animal Disease (FAD), investigate possible incursion scenarios and control options available to manage the outbreak, with a focus on vaccination for FMD and quarantine options for ASF and CSF.
4. Capacity building: Two capacity building activities will be deployed to the local official veterinary services, one on social networks and the second on disease transmission models.
   The postdoctoral researcher will fit models to unique data that has been fully collected and curated. The available dataset includes full animal movement data at farm level, weekly disease occurrence data, complete on-biosecurity, and disease intervention for a span of two years and more. Ultimately, this project will help the local official veterinary services and industry to efficiently preempt and manage endemic and emerging diseases. Overall, the work is expected to provide support to national and international animal health decision-makers and to provide major contributions to our understanding of the ongoing disease spread and in preparation for health emergencies.
   In summary, this work will unravel transmission, estimate epidemic parameters, identify spread determinants, and assess the impact of interventions. In addition, a number of potential research projects are available and so the research focus may be tailored to the interests and skills of the individual. There will also be opportunities to seek grant funding to develop related, independent research. Access to high-powered computing is available through Machado’s lab.
   Environment: The Machado Lab develops and deploys epidemiological methods that fully integrate disease evolution, animal movement, spatial processes to estimate disease transmission and spread. Ultimately we work in the development of new computational surveillance tools for both tracking spread and prescribing treatment regimens. New members, will work in a group with a strong background in epidemiology, disease spread modeling, evolutionary epidemiology, social networks and mathematical modeling. New members will also benefit from the national and international collaboration networks that our lab has developed.

Candidate must have a Ph.D. degree in a relevant quantitative discipline (ecology, epidemiology, applied mathematics, physics, statistics or other quantitative) or closely related field.
The candidate should have moderate to extensive experience in R or other open-source related statistical software and previous work in spatial analysis/network analysis. The candidate should have practical experience with building spatially explicit models, particularly those that are also process-based and mechanistic model development. In addition, the candidate should have evidence of original publications in the peer-reviewed literature. The candidate should have excellent communication skills and the ability to develop grant proposals. Candidates should have the ability to work independently and in collaboration.

This post is a full time

Salary: $49,000

Should you have any queries please contact: Assistant Professor Gustavo Machado (gmachad@ncsu.edu)
For more information about our work visit https://machado-lab.github.io/