Research Associate I

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The Cooperative Institute for Marine and Atmospheric Studies (CIMAS) of the University of Miami (UM) is currently seeking applicants for a full-time Research Associate I position. The successful candidate will participate in research projects that use data from crewed and uncrewed TC reconnaissance missions to improve analysis and prediction of TCs. The first primary focus will be to develop machine learning methods to accurately estimate surface winds within the TC when they are otherwise not available. Candidates should be prepared to develop real-time products to be delivered to the National Hurricane Center for testing and evaluation. A second primary focus will be to quantify the impact of various crewed and uncrewed observing systems on analyses and forecasts of TCs. The candidate will work closely with the National Oceanic and Atmospheric Administration’s (NOAA) Atlantic Oceanographic and Meteorological Laboratory (AOML).

We are searching for an experienced and productive researcher whose primary responsibilities will include:

• •Development, testing, and evaluation of machine learning techniques trained on data gathered within TCs
• •Using the NOAA Hurricane Analysis and Forecast System (HAFS) with various data input sources to evaluate
• •Verification of HAFS forecasts
• •Responsible and effective use of high-performance computing environments
• •Data management and visualization using modern programming languages (e.g., Python)
• •Processing complex data to produce easily understandable results
• •Interpret and publish results in progress reports and peer reviewed literature
• •Present the results at meetings and conferences

Qualified candidates must possess a bachelor’s in Meteorology or Atmospheric Science. Demonstrated familiarity with tropical cyclone reconnaissance data, proficiency in machine learning applications, and running and tuning hurricane models in high-performance computing environments is also required. Familiarity with NOAA operational hurricane models is also desirable, as is proficiency in Python and JavaScript.

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Job Status:

Full time

Employee Type:

Staff