Postdoctoral Associate

MSU Jobs • Miami, FL

1d

About The Position

The successful candidate will contribute to advancing the understanding of cloud microphysics processes within tropical cyclone simulations in the Hurricane Analysis and Forecasting System (HAFS) modeling framework. The ideal candidate has experience working with HAFS and microphysics parameterizations in a numerical modeling context. Additionally, the project will employ machine learning techniques (specifically neural networks), so expertise or experience working with these systems is desirable. Experience with operational meteorology is a plus as well. This is an exciting opportunity to work at the interface of dynamic weather modeling and artificial intelligence in an operationally relevant context.

Requirements

• A Ph.D. in Atmospheric Science, Climate Science, Meteorology, Oceanography, Data Science, or a related field by the start date, with at least one year of relevant experience in atmospheric science research.
• Strong understanding of cloud microphysics within numerical weather prediction systems.
• Experience with dynamic modeling, especially the HAFS model.
• Demonstrated experience with statistical modeling and/or machine learning techniques, with a preference for neural network-based methods.
• Proficiency in scientific programming languages (e.g., Python, R, MATLAB).
• Strong written and verbal communication skills.
• Knowledge in numerical weather prediction systems is required.
• The ideal applicant would need to know how to navigate the Linux environment and understand navigating big data within atmospheric science.
• Additionally, applicants should be familiar with common machine learning techniques utilized within atmospheric science, including but not limited to artificial neural networks and kernel methods.

Nice To Haves

• Experience working with large atmospheric and oceanic datasets.
• Familiarity with tropical cyclone dynamics and prediction.
• Experience in collaborative, interdisciplinary research environments.

Responsibilities

• Quantify and evaluate microphysics conditions within tropical cyclone observations and dynamic model simulations.
• Develop and evaluate statistical and machine learning models (e.g., neural networks) that characterize microphysics processes within tropical cyclones.
• Collaborate with NOAA and university researchers across disciplines.
• Present findings at scientific conferences and publish results in peer-reviewed journals.

What This Job Offers

• Job Type: Full-time
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees