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Job ID: 202113
Location: MONTEREY, CA, United States
Date Posted: Feb 18, 2020
Category: Engineering and Sciences
Subcategory: Research Scientist
Shift: Day Job
Travel: Yes, 10 % of the Time
Minimum Clearance Required: Secret
Clearance Level Must Be Able to Obtain: Secret
Potential for Teleworking: Yes
The candidate will conduct research to develop state-of-the-art data assimilation methods and algorithms, with an emphasis on assimilating classified observations. These observations often have high temporal or spatial density or other unique properties and will thus require the development of specific assimilation strategies and new techniques for assessing observation impact. She/he will conduct research for the data ingest, selection, pre-processing, quality control and assimilation strategies for these specialized observation data sets. Additionally, the candidate will develop methods to assimilate these new observations, and refine the assimilation procedures for currently assimilated observations. The candidate will focus on assimilation strategies using the NRL Atmospheric Variational Data Assimilation System (NAVDAS) 3D-Var and 4D-Var systems.
The candidate work will develop and implement diagnostic methods to assess the impact of the assimilated environmental observations on the subsequent analysis and forecasts. She/he will develop algorithms to summarize and display the results for system users. Additionally, she/he will employ advanced numerical and machine learning techniques to facilitate rapid and comprehensive characterization of the impact of the new observations on the forecast, and identify areas for improvement in the underyling data assimilation system or in the algorithms used to pre-process the observations.
This position requires the ability to conduct and assist in leading-edge research on the development and application of state-of-the-art methodologies for atmospheric data assimilation and forecasting systems and to use these methodologies to develop next generation probabilistic prediction systems primarily using NAVDAS-AR and Navy global and mesoscale modeling systems.
QualificationsTYPICAL EDUCATION AND EXPERIENCE:
Demonstrate extensive knowledge and experience in formulating complex problems and developing and testing of efficient algorithms related to data assimilation.
Have deep knowledge of the principles, methods, and state-of-the-art techniques used in data assimilation.
Effectively apply advanced machine learning techniques to better characterize observation errors and patterns within the data assimilation system, and implement improvements to correct any identified deficiencies.
Lead, plan, conduct, and document research on algorithm development for a portfolio of sophisticated and novel environmentally-based satellite data assimilation algorithms.
Possess in-depth knowledge of Python and MATLAB languages, and the ability to program and work in the UNIX and LINUX operating system environments.
Demonstrate the ability to communicate effectively both orally and in writing, with experience in writing theses, scientific journal articles, or manuals.
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