Meteorology and Machine Learning

Our group is looking for a motivated researcher and software developer with a background in satellite remote sensing and meteorology who would like to work as part of a collaborative, interdisciplinary team of meteorologists, geoscientists, mathematicians, physicists, and computer scientists.  Specifically, the candidate will design, test, and implement algorithms that use satellite data from existing and emerging platforms in an application called Global Synthetic Weather Radar, which is a software system that uses a machine learning model to fuse multiple sources of weather information to produce world-wide synthetic weather radar analyses and forecasts.  A solid knowledge of the principles of applying microwave, visible, and infrared satellite data in meteorological applications is required.  A keen interest in learning about and applying advanced machine learning methods is desired.  The candidate is expected to lead the technical aspects of the tasks and present results to internal and external researchers and technicians. The position will also require the candidate to interface with different agencies and user bases within the US Government in order to discuss technical concepts and present results.

Requirements

• Master of Science degree in atmospheric science or meteorology, physics, or related environmental science. In lieu of a Master’s degree, a Bachelor’s degree with at least three years of directly related research experience will be considered.
• A demonstrated understanding of remote sensing and radiative transfer principles, and experience with applying satellite data to meteorological applications
• Familiarity with algorithm development environments/languages like python (preferred), or MATLAB
• Knowledge in the fields of statistics and probability theory
• Ability to independently research and develop new methods to solve challenging problems of national interest
• Ability to work in a collaborative team environment
• Excellent speaking and writing skills

Desired Skills

• Prior exposure to machine learning algorithms, through coursework or research. Experience with one or several common machine learning, computer vision, or natural language processing toolboxes (PyTorch, Pandas, OpenCV, TensorRT, LibTorch, etc.).
• Experience with software development environments (e.g., Linux, Docker) and best practices and collaboration tools (e.g., github) is a plus.
• Previous experience with building applications and data processing for real-time systems