IRS26127 Maize Breeder job opportunity at CIMMYT.

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Date2026-02-12T17:19:34.521Z bot
CIMMYT IRS26127 Maize Breeder
Experience: 2-years
Pattern: Full-time
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degreeAssociate
loacation Hyderabad, India
loacation Hyderabad....India

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: cimmyt.org CIMMYT is looking for an outstanding, self-motivated, and result-oriented professional for the position of Maize Breeder. This position drive breakthrough breeding advancements in breeding through the application of cutting-edge tools and technologies. The location of this position will be CIMMYT Nairobi, Kenya. Responsibilities Support Maize breeding systems in Asia in alignment with Global breeding strategy. A field-based breeder that drives step change breeding improvements using variety of genetic tools.  Assist in implementing breeding pipelines that use machine learning, genomic prediction and GXEXM. Apply scientific rigor to selection accuracy, cycle time reduction, and gain per year. Ensure contemporary breeding and genetic methods are deployed in breeding pipelines. Contribute to global breeding system design, ensuring harmonization and cross-learning across geographies. Deliver Climate -resilient, resource-efficient germplasm. Integrate Genomics, Molecular breeding and biotechnology. Design and implement MAS and genomic selection, haplotype tracking, DH, speed breeding, high throughput phenotyping and trait introgression methods in breeding pipelines. Collaborate with teams on gene discovery and functional validation projects. Ensure molecular tools are applied to farmer-relevant traits in alignment with program priorities. Drive evidence-based decision making through advanced data insights and visualization. Implement breeder-focused dashboards that evolve into strategic decision tools Translate high-dimensional data into intuitive visuals for breeders, donors, and policymakers. Set global standards for data quality, traceability, and analytics Work closely with data science and informatics teams to deploy AI/ML models for trait prediction, parent selection, and resource optimization. Champion continuous improvement of maize breeding informatics platforms and data governance. Embed Agronomy and Plant Physiology into Predictive Models. Integrate physiological traits and agronomic management into ML-based predictions. Ensure models perform across all target environments including marginal environments where CIMMYT germplasm helps all farmers. Align breeding outputs with sustainable intensification goals.   Partnerships, Representation and Resource Mobilization. Mentor early-career scientists and students in maize breeding including molecular breeding, quantitative genetics, and data science applications. Build institutional capacity & partnerships with NARES partners, SMEs and universities in advanced maize breeding tools. Publish in top-tier journals on breeding and genetic gain, while developing breakthrough hybrids and lines. Guide large-scale proposals with compelling, evidence-based ROI justification Serve as Global Ambassador for CIMMYT Breeding. PhD in Plant Breeding, Genetics, Genomics, Data-Enabled Crop Improvement, or a closely related discipline. Minimum 2 years of post-Ph. D. experience in maize breeding or related crop improvement programs for tropical environments. Demonstrated success in deploying molecular and genomic tools within operational breeding pipelines. Track record of using data-driven and predictive approaches to improve breeding efficiency and genetic gain. Experience in collaborating with NARES, including public & private sector institutions Experience in collaborating across disciplines, including genomics, pathology, physiology, data science, and digital agriculture teams. Evidence of leadership in guiding technical innovation, mentoring scientists, and influencing program strategy. Advanced understanding of molecular breeding and quantitative genetics, including - Marker-assisted selection (MAS), Genomic selection (GS) and genomic prediction, QTL discovery, haplotype analysis etc. Strong applied knowledge of breeding data systems, including breeding informatics and database design, experimental design and multi-environment trial analysis and integration of phenotypic, genotypic, and environmental data Practical knowledge in data science methodologies, such as - statistical modelling and predictive analytics for genetic gain, application of machine learning algorithms for parent selection, line advancement, and performance prediction, working familiarity with AI-enabled breeding tools. Knowledge of data analysis software like R, SAS, and Genstat preferred. Good reporting and communication skills in English (working language). The selected candidate should exhibit the following competencies: Critical and Creative Thinking, Problem Solving and Decision Making, Teamwork, and Communication.

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