Postdoctoral Associate Position in Epidemiology of Microbial Diseases
Position Description
The lab of Dr. Colin Carlson (Epidemiology of Microbial Diseases / Yale Center on Climate Change and Health) is recruiting a Postdoctoral Associate to conduct research on the relationship between global environmental change and infectious disease epidemiology, with a focus on zoonotic and vector-borne diseases.
This position exists as part of the Verena project, an NSF Biology Integration Institute based with the Carlson Lab at Yale. Verena is an international and interdisciplinary collaborative network of researchers with expertise in virology, ecology, and computational biology, working together to develop better (and more biology-driven) quantitative models that can anticipate future risks from emerging infectious diseases. The postdoc will participate in both the Carlson Lab and the Verena program, joining a network led primarily by early career scientists, with over two dozen active trainees.
We are looking for a fellow to conduct research related to the mission of the Verena program. A successful candidate may propose their own research and/or bring existing projects with them, provided they relate broadly to quantitative models of host-virus interactions and viral emergence; they could also develop work in one of these areas:
Geographic range shifts: Species are on the move – and bringing pathogens with them. We’re interested in using open biodiversity datasets to measure historical and present-day geographic range shifts in the arthropod vectors (e.g., ticks, mosquitoes) or vertebrate (e.g., bats) reservoirs of emerging infections; testing hypotheses about the role of climate change; and inferring potential impacts on disease transmission.
Causal inference in ecoepidemiology: A growing proportion of the global burden of disease is the result of human influence on ecosystems and the climate. We’re interested in developing statistical analyses that can estimate the specific contribution of climate and land use change to the burden of zoonotic and vector-borne diseases, with a focus on emerging viruses. We’re also interested in modeling vaccination and other control strategies, and translating these into policy-relevant recommendations.
Mosquito-arbovirus interactions: We’re interested in identifying evolutionary and ecological correlates of vector competence, using machine learning models, annotated mosquito and flavivirus genomes, and a new database compiled from published vector competence experiments. This work will be conducted in close collaboration with the lab of Dr. Gregory Ebel at Colorado State University, who will test predictions and potentially generate transcriptomic and proteomic datasets on mosquito innate immunity.
Literature annotation and disease surveillance: Very little wildlife disease data is currently deposited in FAIR databases. As a result, it is nearly impossible to describe sampling gaps, including those with direct relevance to public health and outbreak risk assessment. We’re interested in borrowing methods from climate science for machine learning classification of scientific literature and using these models to map global research effort in disease ecology and pathogen discovery. This work will be conducted in close collaboration with the lab of Dr. Timothée Poisot at the University of Montreal.
Qualifications
The ideal candidate should have a Ph.D. in epidemiology, ecology, or a related field, and significant experience with statistical analysis and modeling. A strong candidate will be comfortable with R and/or other scientific programming languages (e.g., Python, Julia). Depending on the project, a strong candidate could also have experience with:
- Climate models and climate data, including methods from attribution science
- Genomic data, including genome annotation, and other multi-omic datasets
- Biodiversity data (e.g., GBIF)
- Geospatial models (e.g., R-INLA)
- Machine learning (primarily classification and regression tree models)
- Large language models used in research applications (e.g., BERT)
- Open science platforms (e.g., Github) and best practices
- Software development (e.g., R packages)
Compensation & Benefits:
Postdoc compensation and benefits will be according to Yale policies. More information about salaries and other resources are available.
Application
Please apply online.
Interested candidates should submit a cover letter, CV, and contact information for 3 professional references. The cover letter should address areas of interest and prior experience, as well as at least two recent publications or preprints from the lab that are most closely aligned with the candidate’s interests.
Yale University is an Affirmative Action/Equal Opportunity employer. Yale values diversity among its students, staff, and faculty and strongly welcomes applications from women, persons with disabilities, protected veterans, and underrepresented minorities.