Cooperative Institute for Satellite Earth System Studies (CISESS); Earth System Science Interdisciplinary Center; University of Maryland, College Park
| Date | August 26, 2026 |
| Department | UMD/ESSIC/CISESS |
| Starting Salary | Commensurate with qualifications and experience |
| Starting Title | Faculty Specialist (MS required), Postdoctoral Associate, or Assistant Research Scientist (PhD required), depending on qualifications |
| Closing Date | Open until filled |
| Duty Station | ESSIC, College Park, MD |
About the position
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. The successful candidate will be expected to pursue open scientific questions while supporting current activities the group is involved in.
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 balance among these will be shaped by the interests and strengths of the person hired.
The successful candidate will work within a team that collaborates closely with NOAA, NASA, and partner institutions.
This position is initially for one year, with the possibility of renewal contingent upon performance and funding availability.
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
Qualifications
Required
- Ph.D. in Atmospheric Science, Hydrology, Environmental science, or a closely related field; or an M.S. or higher degree in Computer Science together with demonstrated experience applying machine learning to scientific or otherwise applied problems
- Strong programming ability, particularly in Python
- Practical experience training machine learning models, for example in PyTorch or TensorFlow
- A working understanding of satellite observation of the troposphere across the passive microwave, infrared, and visible spectrum
- Ability to work within an interdisciplinary team and to communicate results clearly in writing and in person
- U.S. Citizenship or Green Card holder (or applicant from a non-designated country due to restrictions at Government facilities)
Desirable
Experience with satellite and ground-based precipitation estimation, validation against weather radar, collocation across satellite sensors, or uncertainty quantification is highly desirable. Experience with large-scale or GPU-based model training is also welcome.
To apply
Interested candidates should apply through the online application form at https://umdsurvey.umd.edu/jfe/form/SV_bsH4hVQKOVGiZhA
where they will be asked to upload a CV and a brief cover letter (half a page) explaining how their qualifications meet the posted requirements.
Review of applications will begin on September 30, 2026 and will continue until the position is filled.
Questions about the position may be directed to Dr. Veljko Petkovic at veljko.petkovic@umd.edu.