Postdoctoral fellowship on high-throughput plant phenotyping

Overview

The Center for Plant Molecular Breeding (CeM²P) invites applications for a Postdoctoral position funded by FAPESP, focused on phenotyping of sugarcane and pasture species. The project emphasizes the use of advanced remote sensing technologies, particularly UAV (drone) imagery data. The selected fellow will develop strategies, tools, and protocols for high-throughput phenotyping to identify and quantify agronomically relevant traits in sugarcane and forage species, such as resistance to biotic and abiotic stresses and vegetative vigor. The fellow will also be responsible for disseminating research results through scientific publications in high-impact journals.

Researchers involved: Prof. Rafael Ribeiro (Institute of Biology, UNICAMP), Prof. Lucas R. Amaral (School of Agricultural Engineering, UNICAMP), Prof. Ricardo da S. Torres (Wageningen University and Research)

On-site position at UNICAMP, Campinas, São Paulo, Brazil

FAPESP postdoctoral fellowship : R$ 12,570.00 per month plus technical reserve

Maximum duration of 4 years, with annual renewal subject to performance evaluation

Contact for inquiries :

Application and eligibility

Applicants must submit their application to Prof. Lucas Amaral ( ), including the following documents:

• Cover letter describing experience and motivation for applying
• CV including publications and major achievements (Lattes CV for Brazilians)
• Research proposal with a maximum length of 4 pages, free format
• PhD degree obtained within the last 7 years
• PhD in one of the following areas: Plant Genetics and Breeding, Agronomy, Agricultural Engineering, Plant Biology, Computer Science, or related fields
• Full-time and exclusive dedication to the project
• Fluency in English
• Relevant scientific publications related to the project theme
• Knowledge of plant phenotyping and remote sensing
• Scientific independence in designing and conducting research, as well as preparing scientific manuscripts
• Familiarity with programming, GIS, and artificial intelligence methods and tools