Statistical Downscaling for Heat & Health Applications

Location
Princeton, New Jersey
Posted
May 25, 2021
Closes
Jun 24, 2021
Career Level
Postdoctoral
Education Level
PhD
Job Type
Full-time
Relocation Cost
Paid
Sector Type
Academia

Princeton University’s Atmospheric and Oceanic (AOS) Sciences Program, in cooperation with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), NOAA’s Climate Program Office (CPO), and NOAA’s National Centers for Environmental Information (NCEI), seeks applications for a postdoctoral or more senior research scientist to conduct research in collaboration with the GFDL Empirical Statistical Downscaling (ESD) science team, the Climate and Health Project Manager of NOAA CPO, and NOAA’s Regional Climate Services Director - Eastern Region. The project seeks to promote the responsive and responsible use of climate projections in applied science studies relevant to NOAA’s health sector stakeholders.

This position is based at NOAA GFDL/Princeton University with regular engagements in the Eastern region. NOAA/OAR/CPO’s Climate and Health program and NOAA/NESDIS/NCEI’s Regional Climate Services program support the development and delivery of place-based climate information products and services to help people make science-informed decisions. The GFDL ESD research team evaluates statistically refined climate projections commonly used in studies of the influence of climate change on topics of societal relevance. The successful candidate’s project will be coordinated with GFDL’s ESD team and will be directly relevant to public health audiences’ use of climate projections for decision-making. Project specifics will be determined by the candidate’s experience and alignment of project goals with NOAA priorities.

We seek applicants with a Ph.D. or equivalent experience in atmospheric, climate, or other physical or environmental sciences, statistics, applied mathematics, or related disciplines. Candidates keenly interested in developing actionable science that transfers knowledge gained from climate models to health decision makers are encouraged to apply. Candidates should be skilled in the application of statistical methods, including uncertainty analysis, and be familiar with North American climate. Programming skills in R or Python are highly desirable, as are strong communication skills. Other beneficial experiences include prior work with downscaled multi-decadal climate projections and/or machine learning methods, and interdisciplinary experience bridging climate science with public health sector applications. This is a full-time, term-limited position with the initial appointment being for one year with the possibility of renewal subject to satisfactory performance and available funding, for a maximum term of three years.

Applicants should apply online https://www.princeton.edu/acad-positions/position/20581.  Complete applications include a cover letter, CV, publication list, and 3 letters of recommendation. Applications should be accompanied by a statement of research interests outlining the candidate’s vision for a research topic bridging statistically downscaled climate projections with applications to the heat and human health sector, including the assessment and communication of uncertainties across disciplines. Application deadline is June 25, 2021, 11:59 pm EST. Review of applications will begin immediately and continue until the position is filled. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community.

For additional information, visit www.gfdl.noaa.gov/heat-and-health-downscaling or contact Keith Dixon (keith.dixon@noaa.gov), Ellen Mecray (ellen.l.mecray@noaa.gov) or Hunter Jones (hunter.jones@noaa.gov).

This position is subject to Princeton University's background check policy.

Princeton University is an equal opportunity/affirmative action employer, and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.