Department of Health and Human Services
National Institutes of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Division of Translational Toxicology
Predictive Toxicology Branch
Spatiotemporal Exposures and Toxicology Group
Research Triangle Park, North Carolina
The National Institute of Environmental Health Sciences (NIEHS) is seeking a candidate for the development and deployment of wildfire-based air pollution exposure models. The position is funded by the CHORDS project at NIEHS, Climate Health Outcomes Research and Data Systems. The successful candidate will lead and support the development of an advanced geospatial model and deployment pipeline for wildfire-based air pollution exposure estimates relevant for human health. Other wildfire or climate related chemical exposure models are also possible if of interest to the candidate. The project will include the development of open-source code, reproducible pipeline development, and deployment of results to facilitate patient-centered and population health research. Fellows will work primarily with Kyle P. Messier, Ph.D., within the Spatiotemporal Exposures and Toxicology Group. Additionally, the candidates will have the opportunity for secondary mentorship and collaboration with other scientists at NIEHS. The candidate is expected to contribute to and author publications in top environmental health journals.
Qualifications
The required skills and expertise for this position are 1) competency in geospatial analysis and spatiotemporal statistical methods, (2) an eagerness to learn new scientific and statistical skills in the environmental health sciences, and (3) demonstrated and on-going ability to contribute to an interdisciplinary and inclusive research group. Most importantly, we are looking for positive, pleasant, and life-long learners.
Preferences will be given to candidates with demonstrated experience in one or more of the following areas:
- Spatial and machine learning methods
- Geospatial Exposure Assessment
- Climate Change and Health Analysis
- Coding in R or other open-source languages
- Code version control using Git
- Reproducible pipeline management
- Linux and high-performance computing cluster environments
- Experience with large, geospatial datasets
- Data wrangling
- Knowledge of geospatial environmental and climate data sets
Applicants are invited at all levels, including B.S., M.S., and Ph.D., and their equivalents.
Applicants should have a degree in geography, statistics, environmental science, public health, computer science or another related field. Expected salary is available. Telework is an option and fully remote is possible for exceptional candidates. The position is open to all U.S. citizens and visa-eligible foreign citizens.
How to Apply
For best consideration applications should be submitted by February 3, 2025. Applications will be considered until the position is filled.
Applicants should submit the following materials to [email protected] with the subject line Geospatial Wildfire Air Pollutant Exposure Modeling:
- Research statement/ cover letter (1-2 pages)
- Curriculum Vitae
- 1-2 recent peer-review publications or writing samples
- 1-2 code examples (optional)
- Contact information for 3 references
Questions about the application may be directed to Kyle Messier, Ph.D., at the application email, [email protected].
Disclaimers
The NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.