Overview
CISESS at the University of Maryland is seeking a Remote Sensing Scientist to develop machine learning models for retrieving precipitation and cloud properties from satellite observations. You'll work with a collaborative team interfacing with NOAA and NASA, advancing retrieval algorithms across passive microwave, infrared, and visible spectrum data.
Job Description
The Cooperative Institute for Satellite Earth System Studies (CISESS) at the University of Maryland seeks a scientist with interest in remote sensing of precipitation and precipitation-related phenomena.
The group works across the passive microwave, infrared, and visible observation of the troposphere, developing retrievals of precipitation and cloud properties, characterizing their uncertainty, and validating against ground-based and spaceborne references.
Active research directions include machine learning approaches to precipitation rate and type retrieval, the quantification of retrieval uncertainty, and the estimation of quantities that satellites do not observe directly. The successful candidate will work within a team collaborating closely with NOAA, NASA, and partner institutions.
Primary Responsibilities
- Develop, train, and evaluate models for geophysical retrieval from satellite observations
- Construct and maintain collocated datasets spanning multiple satellite sensors and ground-based references
- Validate retrievals against ground and spaceborne sensors and characterize retrieval uncertainty
Required Qualifications
- Ph.D. in Atmospheric Science, Hydrology, Environmental Science, or closely related field; or M.S.+ in Computer Science with demonstrated ML experience
- Strong Python programming ability
- Practical experience training ML models (PyTorch or TensorFlow)
- Working understanding of satellite observation across passive microwave, infrared, and visible spectrum
- Ability to work within an interdisciplinary team and communicate clearly in writing and in person
- U.S. Citizenship or Green Card holder
Position is initially one year with possibility of renewal contingent on performance and funding.
Application Deadline: October 8, 2026