Postdoctoral Fellowship: Methodology for mapping and monitoring different integrated crop-livestock systems using remote sensing data

Postdoctoral Fellowship: Methodology for mapping and monitoring different integrated crop-livestock systems using remote sensing data
Location: School of Agricultural Engineering (Feagri), State University of Campinas (Unicamp), Brazil
Job description
The successful candidate will develop, test, and apply different methodological approaches to map and monitor pasture fields and integrated crop-livestock systems (ICLS) in different regions of Brazil using time series of high resolution remotely sensed imagery. The successful candidate will:
� Test different machine and deep learning algorithms to classify and monitor ICLS parcels and other land covers in different study areas in São Paulo and Mato Grosso states;
� Apply the proposed classification approaches to map ICLS fields and other land cover classes in different agricultural years in the study areas;
� Carry out field campaigns for collection of additional reference data to validate the proposed classification approaches;
� Explore different predictor variables derived from optical and Synthetic-Aperture Radar (SAR) remotely sensed imagery to improve the classification of ICLS parcels and the prediction of pasture biomass.
The post-doc is part of the FAPESP project � (FAPESP 2017/50205-9) �Monitoring integrated crop-livestock systems through remote sensing and precision agriculture for more sustainable production - Towards Low Carbon Agriculture�. Several laboratories and institutions collaborate in this project, such as the Interdisciplinary Center for Energy Planning (NIPE), the School of Agricultural Engineering - Feagri (UNICAMP), Embrapa Agricultural Informatics, Universidade do Oeste Paulista - Unoeste, and the Delft University of Technology - TUDelft.
The post-doc will be under the supervision of Dr. João Francisco Gonçalves Antunes (Embrapa Agricultural Informatics ) and will collaborate with the different researchers involved in the project.
The fellowship includes a tax­free monthly stipend of R$ 7.373,10, plus 15% of the yearly value for research related expenses. All conditions and salary related to this FAPESP post-doc are listed there: http://www.fapesp.br/en/postdoc.
Desired skills and competence
� A Ph.D. in engineering / earth sciences / geography / agronomy / computer science, with applications of orbital remote sensing;
� Strong knowledge of satellite image time series, advanced digital image processing, and analysis of remotely sensed data. Experience with PlanetScope and SAR imagery will be an asset;
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� Strong abilities in programming languages (i.e., R and Python) and data analysis techniques;
� Experience with the state-of-art machine and deep learning algorithms for remote sensing applications;
� Experience using desktop GIS and remote sensing software;
� Minimum qualifications include a demonstrated ability to publish peer-reviewed papers, effective oral communication skills, and work well in a collaborative team environment;
� Strong interpersonal skills and adequate fluency in English and in Portuguese;
� Full-time work, initially 09 months, starting in October 2021 (+ 12 months extension);
� All conditions and salary related to this FAPESP post-doc are listed here: http://www.fapesp.br/en/postdoc.
Selection process
Inquiries should be directed by email to Paulo Graziano (grazianog.unicamp.br) with a copy to the NIPE project manager (Johinislan Prates, jpratesunicamp.br), including �FAPESP project 2017/50205-9 post-doc PD2� in the subject line until September 30th, 2021.
Send a cover letter stating your professional experiences and how you would satisfy the minimum qualifications (2 pages max), a CV, and two recommendations letters (all documents in pdf format).
Submissions by email until September 30th , 2021.