{"id":107871,"date":"2026-08-14T09:28:52","date_gmt":"2026-08-14T09:28:52","guid":{"rendered":"https:\/\/jobs.dataaxisnode.com\/kenya\/uncategorized\/ph-d-scholar-rapid-cycling-and-predictive-breeding\/"},"modified":"2026-08-14T09:28:52","modified_gmt":"2026-08-14T09:28:52","slug":"ph-d-scholar-rapid-cycling-and-predictive-breeding","status":"publish","type":"post","link":"https:\/\/jobs.dataaxisnode.com\/kenya\/jobs\/makueni\/ph-d-scholar-rapid-cycling-and-predictive-breeding\/","title":{"rendered":"Ph.D. Scholar &#8211; Rapid Cycling and Predictive Breeding"},"content":{"rendered":"<p><script type=\"application\/ld+json\">{\"@context\": \"https:\/\/schema.org\/\", \"@type\": \"JobPosting\", \"title\": \"Ph.D. Scholar - Rapid Cycling and Predictive Breeding\", \"description\": \"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.\\nA 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.\\nThe 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.\\nThis position will be based at Kiboko, Kenya.\\nDuration: 3\u20134 years, subject to university registration and project funding\\nResearch Focus:\\nThe scholar\u2019s research may address questions such as:\\nDoes rapid cycling at very early generations improve the rate of genetic gain compared with conventional fixed line recycling?\\nHow well do haplotype-guided and genomic prediction-based selections perform across cycles?\\nWhat is the value of sparse testing for prediction accuracy and GxE characterization?\\nCan F1 x F1 rapid cycling accelerate the delivery of superior breeding material?\\nKey Responsibilities:\\nDevelop and implement a Ph.D. research plan around rapid cycling and predictive breeding hypotheses.\\nContribute to evaluating whether accelerated breeding pipelines improve selection efficiency and realized gain.\\nParticipate \u2013 hands on \u2013 in field research comparing predicted performance with observed field outcomes to validate genomic and AI-assisted selection.\\nConduct statistical and quantitative genetic analyses contributing to model evaluation, data visualization, and interpretation of results.\\nLead or contribute to scientific manuscripts suitable for peer-reviewed publication, presentations in meetings or conferences.\\nExpected Outputs:\\nSuccessful writing and defense of a Ph.D. thesis on rapid cycling and predictive breeding.\\nAt least two peer-reviewed publications or manuscripts in preparation.\\nSupervision and Collaboration:\\nThe 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.\\nEligibility Criteria:\\nMaster\u2019s degree in Plant Breeding, Quantitative Genetics, Statistical Genomics, Crop Science, or a closely related field.\\nStrong interest in predictive breeding, rapid cycling, and applied breeding research.\\nAbility to analyze data using R and\/or Python.\\nGood writing, analytical, and problem-solving skills.\\nAbility to work collaboratively with scientists, breeders, and field teams.\\nApplicant 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.\\nGood command of the English language.\", \"datePosted\": \"2026-08-13\", \"hiringOrganization\": {\"@type\": \"Organization\", \"name\": \"International Maize and Wheat Improvement Center CIMMYT\"}, \"jobLocation\": {\"@type\": \"Place\", \"address\": {\"@type\": \"PostalAddress\", \"addressLocality\": \"Makueni\", \"addressCountry\": \"KE\"}}, \"directApply\": true, \"validThrough\": \"2026-08-27T23:59:59\", \"employmentType\": \"CONTRACTOR\"}<\/script><\/p>\n<p><strong>Company:<\/strong> International Maize and Wheat Improvement Center CIMMYT<\/p>\n<p><strong>Location:<\/strong> Makueni<\/p>\n<p><strong>Job Type:<\/strong> Contract<\/p>\n<p><strong>Apply Before:<\/strong> 2026-08-27<\/p>\n<h3>Job Description<\/h3>\n<p>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.<br \/>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.<br \/>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.<br \/>This position will be based at Kiboko, Kenya.<br \/>Duration: 3\u20134 years, subject to university registration and project funding<br \/>Research Focus:<br \/>The scholar\u2019s research may address questions such as:<br \/>Does rapid cycling at very early generations improve the rate of genetic gain compared with conventional fixed line recycling?<br \/>How well do haplotype-guided and genomic prediction-based selections perform across cycles?<br \/>What is the value of sparse testing for prediction accuracy and GxE characterization?<br \/>Can F1 x F1 rapid cycling accelerate the delivery of superior breeding material?<br \/>Key Responsibilities:<br \/>Develop and implement a Ph.D. research plan around rapid cycling and predictive breeding hypotheses.<br \/>Contribute to evaluating whether accelerated breeding pipelines improve selection efficiency and realized gain.<br \/>Participate \u2013 hands on \u2013 in field research comparing predicted performance with observed field outcomes to validate genomic and AI-assisted selection.<br \/>Conduct statistical and quantitative genetic analyses contributing to model evaluation, data visualization, and interpretation of results.<br \/>Lead or contribute to scientific manuscripts suitable for peer-reviewed publication, presentations in meetings or conferences.<br \/>Expected Outputs:<br \/>Successful writing and defense of a Ph.D. thesis on rapid cycling and predictive breeding.<br \/>At least two peer-reviewed publications or manuscripts in preparation.<br \/>Supervision and Collaboration:<br \/>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.<br \/>Eligibility Criteria:<br \/>Master\u2019s degree in Plant Breeding, Quantitative Genetics, Statistical Genomics, Crop Science, or a closely related field.<br \/>Strong interest in predictive breeding, rapid cycling, and applied breeding research.<br \/>Ability to analyze data using R and\/or Python.<br \/>Good writing, analytical, and problem-solving skills.<br \/>Ability to work collaboratively with scientists, breeders, and field teams.<br \/>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.<br \/>Good command of the English language.<\/p>\n<h3>How to Apply<\/h3>\n<p>Interested and qualified? Go to International Maize and Wheat Improvement Center (CIMMYT) on apply.workable.com to apply<br \/>Build your CV for free. Download in different templates.<\/p>\n<p><a href=\"https:\/\/www.myjobmag.co.ke\/apply-now\/1305977\" target=\"_blank\" rel=\"noopener\" style=\"display:inline-block;padding:10px 20px;background:#2271b1;color:#fff;text-decoration:none;border-radius:4px;\">Apply Now<\/a><\/p>\n<p><small>Source: MyJobMag<\/small><\/p>\n<p><!-- job-expiry: 2026-08-27 --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Company: International Maize and Wheat Improvement Center CIMMYT Location: Makueni Job Type: Contract Apply Before: 2026-08-27 Job Description 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, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1408,1763],"tags":[1443,2077,1413,1412],"class_list":["post-107871","post","type-post","status-publish","format-standard","hentry","category-jobs","category-makueni","tag-contract","tag-international-maize-and-wheat-improvement-center-cimmyt","tag-job-listing","tag-myjobmag"],"_links":{"self":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts\/107871","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/comments?post=107871"}],"version-history":[{"count":0,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts\/107871\/revisions"}],"wp:attachment":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/media?parent=107871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/categories?post=107871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/tags?post=107871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}