The International Maize and Wheat Improvement Center, known by its Spanish acronym, CIMMYT®, is a not-for-profit research and training organization with partners in over 100 countries. Please refer to our website for more information: www.cimmyt.org

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.

A key part of the project focuses on testing rapid cycling schemes such as recycling at very early generations (F1) and using haplotype-based cross prediction to select individuals to generate succeeding cycles. The project also emphasizes learning loops, where early-generation predictions are compared with conventional fixed line predictions and realized field performance to refine breeding decisions and improve future cycles.

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. The research will focus on testing hypotheses related to recurrent selection, F1-based rapid cycling, sparse testing, and the translation of genomic predictions into realized field performance.

This position will be based at Kiboko, Kenya.

Duration: 3–4 years, subject to university registration and project funding

Research Focus:

The scholar’s research may address questions such as:

• Does rapid cycling at very early generations improve the rate of genetic gain compared with conventional fixed line recycling?
• How well do haplotype-guided and genomic prediction-based selections perform across cycles?
• What is the value of sparse testing for prediction accuracy and GxE characterization?
• Can F1 x F1 rapid cycling accelerate the delivery of superior breeding material?

Key Responsibilities:

• Develop and implement a Ph.D. research plan around rapid cycling and predictive breeding hypotheses.
• Contribute to evaluating whether accelerated breeding pipelines improve selection efficiency and realized gain.
• Participate – hands on – in field research comparing predicted performance with observed field outcomes to validate genomic and AI-assisted selection.
• Conduct statistical and quantitative genetic analyses contributing to model evaluation, data visualization, and interpretation of results.
• Lead or contribute to scientific manuscripts suitable for peer-reviewed publication, presentations in meetings or conferences.

Expected Outputs:

• Successful writing and defense of a Ph.D. thesis on rapid cycling and predictive breeding.
• At least two peer-reviewed publications or manuscripts in preparation.

Supervision and Collaboration:

The Ph.D. scholar will be supervised by a CIMMYT scientist and a university academic supervisor. The scholar will work closely with the project lead scientist and staff on activities related to RCGS validation and pipeline implementation.

Eligibility Criteria:

• Master’s degree in Plant Breeding, Quantitative Genetics, Statistical Genomics, Crop Science, or a closely related field.
• Strong interest in predictive breeding, rapid cycling, and applied breeding research.
• Ability to analyze data using R and/or Python.
• Good writing, analytical, and problem-solving skills.
• Ability to work collaboratively with scientists, breeders, and field teams.
• Applicant should be enrolled or agree to enroll in a university in Africa, with thesis work in Kiboko, Kenya, and with opportunities to travel to project scope countries ( Ethiopia, Tanzania).
• Good command of the English language.

• Develop and implement a Ph.D. research plan around rapid cycling and predictive breeding hypotheses.
• Contribute to evaluating whether accelerated breeding pipelines improve selection efficiency and realized gain.
• Participate – hands on – in field research comparing predicted performance with observed field outcomes to validate genomic and AI-assisted selection.
• Conduct statistical and quantitative genetic analyses contributing to model evaluation, data visualization, and interpretation of results.
• Lead or contribute to scientific manuscripts suitable for peer-reviewed publication, presentations in meetings or conferences.

• Ability to analyze data using R and/or Python.
• Good writing, analytical, and problem-solving skills.
• Ability to work collaboratively with scientists, breeders, and field teams.
• Good command of the English language.

• Master’s degree in Plant Breeding, Quantitative Genetics, Statistical Genomics, Crop Science, or a closely related field.
• Strong interest in predictive breeding, rapid cycling, and applied breeding research.
• Applicant should be enrolled or agree to enroll in a university in Africa, with thesis work in Kiboko, Kenya, and with opportunities to travel to project scope countries ( Ethiopia, Tanzania).