Jobs at 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.

Responsibilities

• Collaborate with quantitative geneticists, breeders, technicians, and partners to translate genomic and predictive analytics into crossing and population development decisions.
• Support the senior breeder in the deployment of advanced breeding innovations across the ADCIN sorghum network in ESA, including recurrent and rapid-cycle genomic selection, and the identification, crossing, selection, and advancement of breeding populations.
• Coordinate sparse multi-location testing and multi-environment validation across target environments, working with partners on trial planning, site selection, deployment, and phenotyping.
• Support high-quality data generation through trial management, quality control, harvest, and sample handling, and collaborate with biometricians and quantitative geneticists on data analysis and interpretation.
• Support the integration of predictive breeding and data-driven decision-making into routine breeding pipelines across CIMMYT and partner programs.
• Prepare and contribute to scientific publications, technical reports, presentations, capacity Development, and other knowledge products that document activities, results, and outcomes.

Requirements

• PhD in Plant Breeding, Agronomy, or a closely related discipline, obtained within the least five years, or expected to graduate within the next six months.
• Experience in field-based breeding research, multi-environment trials, or breeding pipeline implementation in sorghum.
• Strong working knowledge of genomic selection, breeding data systems, and quantitative genetics.
• Demonstrated ability to work with breeders, field teams, and data analysts in applied breeding environments.
• Proven track record in publications.
• Strong written and verbal communication skills in English.
• Experience with sparse testing, RCGS, predictive breeding, or rapid generation advancement in sorghum are desirable.

Skills and attitudes

• Field research coordination, leadership and implementation skills.
• Translation of advanced breeding concepts into practical operational decisions.
• Problem-solving and attention to detail.
• Teamwork and collaboration across institutions and disciplines.
• Initiative, adaptability, and willingness to work in field and analytical environments.

go to method of application »

• 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.