Postdoctoral Researcher — Aquatic Remote Sensing & ML

RFCUNY Research Foundation of the City University of New York; United States; On-site; USD 65,000 - 72,000; Full time

Job summary

RFCUNY invites applications for a Postdoctoral Research Associate to join an interdisciplinary team focusing on aquatic remote sensing and machine learning. The position centers on developing computational workflows to study water-quality parameters in inland systems using satellite and UAV imagery.

The successful candidate will integrate multi-sensor data, validate methods, and contribute to publications, reports, and conference presentations, with opportunities to mentor students and

Qualifications

• Ph.D. in Environmental Science, Remote Sensing, or a closely related field (awarded by start date).
• Demonstrated experience with satellite or drone remote sensing for environmental or aquatic systems.
• Proficiency in scientific programming (Python, R, or equivalent).
• Knowledge of machine‑learning methods applied to environmental or geospatial data.
• Excellent writing, analytical, and communication skills, with a record of scholarly publications.

Responsibilities

• Develop and validate methods for extracting surface temperature, optical water-quality indicators, and other parameters.
• Apply machine-learning tools for spatiotemporal analysis of aquatic systems.
• Contribute to data integration, QA procedures, and scholarly dissemination via publications and conferences.
• Collaborate with faculty, students, and partner institutions on data synthesis and proposal development.

Skills

Satellite remote sensing

Drone remote sensing

Scientific programming (Python/R)

Machine-learning for geospatial data

Scholarly writing & communication

Education

Ph.D. in Environmental Science/Remote Sensing or related field

Job description

RFCUNY invites applications for a Postdoctoral Research Associate to join an interdisciplinary team focusing on aquatic remote sensing and machine learning. The position centers on developing computational workflows to study water-quality parameters in inland systems using satellite and UAV imagery.

The successful candidate will integrate multi-sensor data, validate methods, and contribute to publications, reports, and conference presentations, with opportunities to mentor students and