{"id":45927,"date":"2022-05-05T08:40:55","date_gmt":"2022-05-05T15:40:55","guid":{"rendered":"https:\/\/sites.evergreen.edu\/mesweekly\/?p=45927"},"modified":"2022-05-05T08:40:55","modified_gmt":"2022-05-05T15:40:55","slug":"phd-postdoc-position-in-ocean-biology-data-processing-and-evaluation-goddard-earth-sciences-technology-and-research-washington-d-c","status":"publish","type":"post","link":"https:\/\/sites.evergreen.edu\/mesweekly\/phd-postdoc-position-in-ocean-biology-data-processing-and-evaluation-goddard-earth-sciences-technology-and-research-washington-d-c\/","title":{"rendered":"PhD: Postdoc Position in Ocean Biology Data Processing and Evaluation, Goddard Earth Sciences Technology and Research (Washington, D.C.)"},"content":{"rendered":"

For more information, please follow this link:<\/strong> https:\/\/environment.uw.edu\/career-opportunities\/postdoc-position-in-ocean-biology-data-processing-and-evaluation\/<\/a><\/p>\n

Salary<\/strong>– DOE<\/p>\n

The Goddard Earth Sciences Technology and Research II (GESTAR II)
\nconsortium at NASA\u2019s Goddard Space Flight Center (GSFC) invites applications for a
\npostdoctoral position in the Ocean Ecology Laboratory (Code 616) at NASA Goddard Space Flight Center. The lab conducts research on ocean ecosystems and the interactions between Earth\u2019s chemical cycles and life. The laboratory primarily manages \u201cocean color\u201d data from NASA satellite sensors. The candidate will join the Ocean Biology Processing Group (OBPG) and focus on the development and evaluation of bio-optical algorithms for derivation of aquatic inherent optical
\nproperties (IOPs) from MODIS, VIIRS, OLCI, SGLI, and PACE.<\/p>\n

The successful candidate will join the GESTAR II Consortium which supports over 120 researchers based primarily at NASA Goddard Space Flight Center (GSFC). GESTAR II researchers work to create extensive opportunities for breakthroughs in earth and atmospheric science research, carrying out observational, experimental and theoretical research in support of NASA strategic Earth Science mission objectives.<\/p>\n

Goddard\u2019s Earth Science Division is home to about 200 civil servants and over 1200 collaborating researchers and support personnel dedicated to studying the Earth as an integrated system that includes the atmosphere, oceans, biosphere, cryosphere, and geosphere. The Division operates as a component of the Sciences and Exploration Directorate that collaborate on interdisciplinary research with the Astrophysics Science, Heliophysics Science, and Solar System Exploration Divisions.<\/p>\n

The researcher will focus on:<\/strong><\/p>\n

\u2013 developing and evaluating updates to the Generalized Inherent Optical Property (GIOP) software framework,
\n\u2013 evaluating and refining existing QA\/QC metrics and skill assessment procedures,
\n\u2013 evaluating in situ and other satellite data streams related to bio-optical algorithm development, assembly of data sets, evaluation and intercomparison of time-series,
\n\u2013 contributing to reporting of algorithm retrieval skill via the OBPG\u2019s ocean color satellite-to-in situ validation system,
\n\u2013 collaborating with OBPG staff on general ocean color satellite calibration and validation activities,
\n\u2013 presenting at NASA and professional society meetings and documenting work in peer-reviewed literature, and
\n\u2013 conducting periodic independent research and assisting with NASA proposal writing as appropriate.<\/p>\n

Anticipated specific research duties include investigation of multivariate models and machine learning techniques for use in IOP retrievals and the extension of bio-optical algorithms to ESA\/EUMETSAT OLCI and JAXA SGLI satellite data streams.<\/p>\n

Required knowledge and skills:<\/strong><\/p>\n

\u2013 PhD in oceanography, physics, mathematics, or a related field,
\n\u2013 research experience in bio-optics, remote-sensing, and radiative transfer theory,
\n\u2013 demonstrated working knowledge of inverse models, multivariate modeling and\/or machine learning algorithms,
\n\u2013 proficiency in one or more programming languages such as Python\/Matlab\/R\/Julia for the analysis of scientific data,
\n\u2013 experience in NASA\u2019s SeaDAS or related satellite data-processing software, and
\n\u2013 good oral and written communication skills demonstrated by a publication record in peer-reviewed literature and\/or scientific reports as first author, and a record of presenting research outcomes at seminars\/meetings\/conferences.<\/p>\n

Desired knowledge and skills :<\/strong><\/p>\n

\u2013 Familiarity with Sentinel-3 OLCI and JAXA SGLI ocean color datasets,
\n\u2013 experience with measurement uncertainty propagation\/estimation techniques,
\n\u2013 experience working in Unix\/Linux software environments,
\n\u2013 proficiency in FORTRAN, C, or C++ programming language, and
\n\u2013 experience using version control software git\/svn.<\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":9433,"featured_media":34098,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_s2mail":""},"categories":[15],"tags":[4,28,33,141,13],"_links":{"self":[{"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/posts\/45927"}],"collection":[{"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/users\/9433"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/comments?post=45927"}],"version-history":[{"count":1,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/posts\/45927\/revisions"}],"predecessor-version":[{"id":45928,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/posts\/45927\/revisions\/45928"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/media\/34098"}],"wp:attachment":[{"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/media?parent=45927"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/categories?post=45927"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.evergreen.edu\/mesweekly\/wp-json\/wp\/v2\/tags?post=45927"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}