Postdoctoral Fellowship in Remote Sensing – Brazil

About Position/Job

FAPESP – São Paulo Research Foundation, in collaboration with the National Institute for Space Research (INPE), is offering a 36-month Postdoctoral Fellowship in Remote Sensing and Applied Computing based in São José dos Campos, Brazil. This position is hosted at the Laboratory of Instrumentation in Aquatic Systems (LabISA) and is part of the international WaterWeave Project, which focuses on innovative strategies for monitoring and the sustainable management of water resources.

The fellowship centres on the application of remote sensing, machine learning, and cloud computing to estimate water quality parameters and predict cyanobacteria blooms in the Tietê River reservoir system. The role offers a strong interdisciplinary research environment, combining geosciences, environmental sciences, and applied computing, with opportunities for field activities, large-scale data processing, and international scientific collaboration.

Position/Job Details

Position: Postdoctoral Fellow
Institution: FAPESP – São Paulo Research Foundation
Department: Laboratory of Instrumentation in Aquatic Systems (LabISA), INPE
Research Field: Geosciences
Location: São José dos Campos, Brazil
Number of Posts: 1
Job Type: Postdoctoral Fellowship (Full-time)
Start Date (Anticipated): 2026
Working Hours: Not specified
Salary Range: R$ 12,570.00 per month + research contingency fund
Required Degree/Diploma: PhD

Research Area

Remote sensing of water quality
Machine learning for environmental monitoring
Cyanobacteria bloom prediction
Cloud computing and big data processing
Sustainable water resource management

Eligibility & Qualifications

Minimum Requirements:

PhD in Remote Sensing, Environmental Sciences, Computer Science, Engineering, or a related field
Strong knowledge of remote sensing, preferably in water quality applications
Proven programming experience, preferably in Python

Preferred Qualifications:

Experience with R or MATLAB
Familiarity with cloud computing and large-scale data platforms such as Google Earth Engine, BDC, or AWS

Key Responsibilities

Develop machine learning models to estimate water quality parameters using remote sensing data
Create predictive algorithms for cyanobacteria bloom occurrence
Support field activities and process large environmental datasets
Contribute to peer-reviewed scientific publications