Physical Scientist – Advanced Coastal Modeling and Dev. Support

OAI is hiring up to two physical scientists to support NOAA Office of Coast Survey (OCS) Coastal Marine Modeling Branch (CMMB). Successful candidates will be involved in development and testing of shelf-scale and regional operational ocean data assimilation and forecast systems. These systems provide forecasts of the currents, temperature and salinity within the US Exclusive Economic Zone, and are used as guidance for navigation, fisheries operations, search and rescue, and environmental hazard response. Outputs from these forecast systems provide boundary and initial conditions information for higher resolution coastal ocean models. The immediate focus will be on testing improvements in the US West Coast Ocean Forecast System (WCOFS), considering improvements to model physics and data assimilation schemes.

The OCS’s Coast Survey Development Laboratory (CSDL) manages the development and improvement of charting, hydrographic, and oceanographic systems used by OCS, the Center for Operational Oceanographic Products and Services, and other NOS offices/programs. CSDL reviews and interacts with research and development programs in private industry, universities, and government, both nationally and internationally. Based on its awareness of state-of-the-art technical developments, CSDL recommends and develops future methods and systems to enhance NOAA and the quality and usefulness of its products. The NOS Storm Surge Modeling team within CSDL develops modeling techniques and infrastructures for end users to perform disaster risk assessments and prediction capabilities to reduce and mitigate coastal vulnerability. These physical scientist positions will provide overall support to this team and assist in the design and development of an operational coupled hydrodynamic and wave modeling system.

Required Skills /Education/ Certifications & Qualifications:

• Ph.D. in Computer Science, Atmospheric Sciences, Mechanical Engineering, Oceanography, Physics, or a related field, and a minimum of 4 years of directly related experience; or a MS/MA plus a minimum of 8 years of directly related experience;
• Knowledge/understanding of mathematical concepts relevant to data assimilation theory (e.g., multivariate calculus, linear algebra);
• Understanding of oceanographic processes such as tidal and wind driven flows, upwelling, and the transport and mixing of riverine flows;
• Experience developing and running environmental (ocean circulation, weather, hydrological, engineering flow) computer models (e.g, ROMS);
• Strong programming skills in Matlab and/or Python.

Preferred qualifications:

• Experience with Fortran, MPI, and Linux;
• Experience working in high performance computing environments;
• Background in data assimilation schemes in oceanographic, atmospheric, or geophysical contexts;
• Familiarity with satellite and in-situ oceanographic observations.

Salary: This is a full-time position. Wage range is $37 to $58 per hour. Overtime is not anticipated. Comprehensive benefits package.

Location: NOAA Facilities in Silver Spring, Maryland (NOAA Headquarters); Seattle, Washington; and Norfolk, Virginia. If necessary or found feasible by the government, contract staff may also be located at other NOAA facilities. Remote work may be approved on a case-by-case basis.

Start Date: As soon as possible.

Only qualified applicants that meet minimum experience or background requirements stated above need apply. When applying for this position you will be asked to upload your resume at the end of this online application.

Applicants should submit a resume that includes the following:

• Cover letter that briefly describes how you meet the required and preferred qualifications listed.
• Work history for the past 10 years or since last full-time education.
• Education.
• Previous experience or training with similar requirements.
• Three professional references.
• Include your name in the document file name.
• Upload your resume in readable, not scanned, PDF or Word format (PDF is preferred).

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