Ph.D. Scholar - Rapid Cycling and Predictive Breeding
AI summary
CIMMYT is recruiting a Ph.D. scholar to research rapid cycling and AI-assisted predictive breeding for dryland crops in Kenya. The role is based at Kiboko and focuses on testing whether accelerated breeding pipelines improve genetic gain, prediction accuracy, and delivery of climate-resilient varieties. The position runs for 3–4 years, subject to university registration and project funding.
- Research-focused Ph.D. role in rapid cycling and predictive breeding
- Based at Kiboko, Kenya
- 3–4 year duration linked to university registration and funding
- Requires Master’s in Plant Breeding, Quantitative Genetics, or related field
- Hands-on field and statistical work using R and/or Python
- Supervised by a CIMMYT scientist and a university academic supervisor
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Original source description
CIMMYT is a cutting edge, non-profit, international organization dedicated to solving tomorrow's problems today. It is entrusted with fostering improved quantity, quality, and dependability of production systems and basic cereals such as maize, wheat, triticale, sorghum, millets, and associated crops through applied agricultural science, particularly in the Global South, through building strong partnerships. This combination enhances the livelihood trajectories and resilience of millions of resource-poor farmers, while working towards a more productive, inclusive, and resilient agrifood system within planetary boundaries.
For more information, visit
https://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 Qualifications
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.
- 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.
- 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.
Requirements
of the host university and CIMMYT policies and procedures.
Candidates
must apply online
. Applications must include the following documents:
CV, cover letter, proof of admission to a PhD program in Plant Breeding, Genetics, or a related field, and an endorsement letter from their university professor or advisor.
Please note that only short-listed candidates will be contacted.
This position will remain open until filled.
CIMMYT is an equal opportunity employer. It fosters a multicultural work environment that values gender equality, teamwork, and respect for diversity. Women are encouraged to apply.
At Impactpool we do our best to provide you the most accurate info, but closing dates may be wrong on our site. Please check on the recruiting organization's page for the exact info. Candidates are responsible for complying with deadlines and are encouraged to submit applications well ahead.
Before applying, please make sure that you have read the
for the position and that you qualify. Applications from non-qualifying applicants will most likely be discarded by the recruiting manager.
Summary by Impactpool
CIMMYT is seeking a Ph.D. scholar to enhance dryland crop breeding through rapid cycling and predictive breeding techniques. The scholar will conduct research to evaluate the efficiency of accelerated breeding pipelines and the accuracy of genomic predictions.
Master’s degree in Plant Breeding, Quantitative Genetics, Statistical Genomics, or related field
Strong interest in predictive breeding 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 and field teams
Enrollment or agreement to enroll in a university in Africa
Good command of the English language
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Ph.D. Scholar - Rapid Cycling and Predictive Breeding
CIMMYT - International Maize and Wheat Improvement Center
Kenya
Both national and international
Internship level
Speaks English
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