2 postdoc positions and 1 technical training position in Geoinformatics and Remote Sensing

European Geosciences Union; Piracicaba; Teletrabalho; BRL 20.000 - 80.000; Tempo integral

Resumo da oferta

An established industry player is seeking talented postdoctoral researchers and a technical trainee to contribute to a groundbreaking project aimed at restoring landscapes in São Paulo. This innovative initiative focuses on developing a geospatial decision support platform, offering a unique opportunity to work with interdisciplinary teams and make a significant impact on environmental restoration. Candidates will engage in cutting-edge research, utilizing remote sensing and geospatial modeling techniques to identify priority areas for restoration. Join this forward-thinking program and help shape the future of ecological sustainability!

Qualificações

• Expertise in geospatial modeling and remote sensing required.
• Proficiency in Python and SQL for data analysis.

Responsabilidades

• Develop multicriteria spatial models for restoration prioritization.
• Collect and integrate remote sensing data for monitoring.

Conhecimentos

Geospatial modeling

Remote sensing

Python

SQL

Machine learning

Deep learning

WebGIS

Formação académica

PhD

Bachelor's degree

Master's degree

Ferramentas

GNSS

Drones

Descrição da oferta de emprego

2 postdoc positions and 1 technical training position in Geoinformatics and Remote Sensing

Employer

Energy, Resources and the Environment (ERE)

Type

Contract

Level

Experienced

R$ 12,000/month, 36 months

Required education

PhD

25 May 2025

Posted

6 May 2025

FAPESP Project: RedeFloresta: Geospatial decision support platform for the restoration of landscapes and ecosystems in the state of São Paulo (Public Policy Research Program – PPPP)

FAPESP process number: 2023/09862-7

Supervisor: Prof. Pedro Henrique Santin Brancalion

Co-Supervisors: Profª. Catherine Torres de Almeida (UFPR), Prof. Paulo Guilherme Molin (UFSC)

The ReflorestaSP program, from the Secretariat of Environment, Infrastructure and Logistics of São Paulo (SEMIL), aims to increase vegetation coverage by 700 thousand hectares by 2050. To optimize socio-ecological benefits and minimize costs, strategic planning for restoration is essential.

The RedeFloresta project aims to develop an online decision-making platform to identify priority areas for restoration and monitor interventions using remote sensing data. We seek 2 postdoctoral candidates with expertise in geospatial modeling (PD#1) and remote sensing (PD#2), and 1 TT4 (technical training) position for professionals to support database creation and field data collection with drones and GNSS.

Selected candidates will collaborate with interdisciplinary teams from ESALQ/USP, UFPR, UFSC, UNESP, and other institutions, working directly with SEMIL and NGOs like PACTO, SOS Mata Atlântica, WWF, IPE, and TNC. The workplace is at ESALQ/USP in Piracicaba/SP, with occasional travel to São Paulo/SP and Curitiba/PR.

The postdoctoral fellowship (R$12,000/month) lasts 36 months, requiring a PhD. The TT4 fellowship (R$4,320/month) lasts 24 months, requiring a bachelor’s degree with 2+ years of experience or a master’s degree, with full-time dedication. Positions are open to Brazilian and foreign researchers; proficiency in Portuguese is required.

Application involves submitting a letter of interest, references, curriculum, and documents proving the degree. For inquiries, contact [e-mail].

Positions:

• PD#1: Developing multicriteria spatial models to prioritize restoration areas, requiring knowledge of geospatial modeling and programming (Python, SQL), and experience with geospatial databases and WebGIS is desirable.
• PD#2: Remote sensing data collection and integration using machine learning/deep learning for monitoring restoration, requiring remote sensing and programming skills, with experience in forest inventory and WebGIS desirable.
• TT4: Creating and organizing geospatial databases and field data collection with drones and GNSS; knowledge of WebGIS development is desirable.