International Maize and Wheat Improvement Center CIMMYT

The successful candidate will provide hands-on technical leadership for practical breeding operations, including collaborating with quantitative geneticists for population development, sparse multi-location testing, and field validation of prediction outputs. The role will work closely with the ESA ADCIN network of breeders on nursery and trial execution to enable selection decisions for accelerated population development across target environments. This position will strengthen the interface between advanced analytics and field delivery by linking quantitative tools, breeding operations, and regional partner implementation within a shared breeding framework, contributing to CGIAR's broader emphasis on modern, efficient breeding systems, stronger national and regional partnerships, capacity development, and faster delivery of climate-resilient crop improvement solutions for smallholder farmers in Africa.

CIMMYT is implementing a project to enhance the speed and precision of dryland crop breeding through artificial intelligence, genomic prediction, rapid generation advancement, and rapid cycling genomic selection (RCGS). The project aims to radically shorten breeding cycles, increase genetic gain, and improve the delivery of climate-resilient, market-preferred varieties for dryland farming systems. The Ph.D. scholar will generate research evidence on whether rapid cycling and AI-assisted breeding improve breeding efficiency, prediction accuracy, and genetic gain in dryland crops.