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Opportunity at National Oceanic & Atmospheric Administration (NOAA)

Big Data, Artificial Intelligence, and Machine Learning for Environmental Data Stewardship

Location

National Environmental Satellite, Data & Information Service, National Centers for Environmental Information

RO# Location
26.01.33.B5760 Silver Spring, MD 20910

Advisers

Name E-mail Phone
Casey, Kenneth Scott kenneth.casey@noaa.gov 301.713.4849

Description

Synoptic views of the Earth’s atmosphere and oceans from satellite sensors and new in situ observing technologies can be used to study climate change, the role of the ocean in the carbon cycle, and the link between ecosystem development and the physics of the mixed layer. With the growing volumes and complexity of environmental data, effective and accessible archives have become more important than ever. Big Data, Artificial Intelligence (AI), and environmental informatics activities at the National Centers for Environmental Information (NCEI, formerly the National Oceanographic Data Center, the National Climatic Data Center, and National Geophysical Data Center) support the scientific goals of climate, atmospheric, and oceanographic research by enabling the effort discovery and use of state-of-the-art satellite and in situ data sources of surface temperature, sea level, and many other parameters. Research opportunities exist to develop and apply Big Data, AI, and Machine Learning (ML) techniques to improve the discovery, accessibility, and use of in situ and satellite datasets in various scientific problems.

 

Keywords:
Big Data; Informatics; Satellite remote sensing; Satellite oceanography; Climate; Artificial Intelligence; Machine Learning

Eligibility

Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens
Level:  Open to Postdoctoral and Senior applicants

Stipend

Base Stipend Travel Allotment Supplementation
$45,000.00 $2,000.00

Experience Supplement:
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.

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