CSS Seeks a post-doctoral scientist to conduct research on "Blue Carbon Valuation of Nature-Based Infrastructure" at the NOAA National Centers for Coastal Ocean Science (NCCOS).
The NCCOS Beaufort Lab conducts basic and applied research on: 1) the response of intertidal habitats to sea level rise and other climate change-related stressors, and; 2) the use of natural coastal habitats to promote resilience to these stressors.
The successful candidate will join a collaborative team of researchers investigating carbon burial (aka blue carbon) in coastal natural infrastructure. The overarching goal is to evaluate the potential of blue carbon burial to be quantified and considered in cost-benefit decisions regarding the use of natural infrastructure vs. traditional built infrastructure solutions (ie. shoreline hardening). The applicant will have the opportunity to interact with collaborators in academic and governmental sectors, and leeway to influence the direction of the project within broad boundaries. Project partners include Florida International University - Science, Sea Level Solutions Center; US Army Corps of Engineers - Engineer Research and Development Center.
Design and implement a research effort to quantify rates of carbon burial in vegetated natural infrastructure systems (marshes and/or coastal islands).
Plan, participate in, and oversee field and laboratory components of this effort. Two to three weeks of travel per year will be required for field work.
Synthesize and interpret results in the context of applied management decisions
Lead and contribute to peer-reviewed publication, presentation and technical memoranda.
D. with research experience and background in coastal wetland ecology, biogeochemistry of nearshore and intertidal sediments or blue carbon.
Demonstrated excellence in written and oral communication of scientific results
Demonstrated experience working independently and as part of a team
Ability to work effectively in dynamic, multi-project, multi-collaborator setting
Experience with statistical analysis of ecological data
Ecological Modeling experience is desirable
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