Postdoctoral Researcher in Passive Microwave Precipitation Remote Sensing — ESSIC at the University of Maryland

Overview

ESSIC at the University of Maryland is seeking a postdoctoral researcher to advance passive microwave remote sensing technology for precipitation monitoring. You'll work with NASA's Goddard Space Flight Center's Mesoscale Atmospheric Processes Laboratory, developing satellite retrieval algorithms and contributing to cutting-edge atmospheric science research.

Job Description

ESSIC at the University of Maryland seeks a postdoctoral researcher with strong interest and experience in passive microwave remote sensing and modeling. The successful candidate will contribute to projects for the Mesoscale Atmospheric Processes Laboratory (code 612) at NASA's Goddard Space Flight Center.

Responsibilities include satellite precipitation retrieval development and validation, microwave land surface emissivity model development, optimization of precipitation retrievals for land surfaces and snow cases, radiometer constellation calibration, and contributions to peer-reviewed publications and agency working groups.

Qualifications: Ph.D. in Atmospheric Science, Computer Science, Physics, Environmental Science, Engineering or related field. Experience with radiative transfer (microwave), satellite data assimilation and retrieval techniques. Proficiency in Python; Fortran a plus. Familiarity with netCDF, HDF5, Unix/Linux. Machine learning experience highly desirable.

U.S. Citizenship, Green Card holder, or applicant from a non-designated country required.

Summary

Key Responsibilities

•Develop and validate satellite precipitation retrieval algorithms using passive microwave remote sensing data
•Build and optimize microwave land surface emissivity models to improve precipitation retrieval accuracy over complex terrain and snow-covered regions
•Calibrate and optimize radiometer constellation data for consistent multi-satellite precipitation measurements
•Conduct radiative transfer modeling and analysis to support algorithm development and validation efforts
•Author and contribute to peer-reviewed publications and participate in NASA agency working groups to disseminate research findings
•Collaborate with atmospheric scientists and engineers to integrate machine learning techniques into retrieval algorithms

Key Requirements

•Ph.D. in Atmospheric Science, Computer Science, Physics, Environmental Science, Engineering, or closely related field
•Demonstrated experience with radiative transfer modeling in the microwave spectrum and passive microwave remote sensing
•Proficiency in Python programming; Fortran experience highly desirable
•Working knowledge of satellite data formats (netCDF, HDF5) and Unix/Linux operating systems
•Experience with satellite data assimilation and precipitation retrieval algorithm development
•Machine learning expertise (TensorFlow, PyTorch, scikit-learn, etc.) is highly desirable; U.S. Citizenship, Green Card, or eligibility from non-designated country required