Individual Consultant (Yam Geneticist) at IITA - International Institute of Tropical Agriculture

Description


The International Institute of Tropical Agriculture (IITA) is seeking to engage the services of an Individual Consultant to support the development and application of advanced analytical tools for accelerating genetic gain in yam (Dioscorea spp.).
IITA is implementing innovative breeding strategies that integrate genomic selection, genotype‑phenotype prediction, and high‑throughput phenotyping to enhance breeding efficiency and strengthen decision‑making within the yam improvement program.
The purpose of this assignment is to provide expert guidance in designing, implementing, and validating cutting‑edge quantitative genetic models, while also building staff capacity in modern statistical and computational genetics.
The Consultant will contribute to improving prediction accuracy, optimizing breeding pipelines, and co‑authoring scientific publications that highlight the advancements and findings from this work.
This assignment is critical to advancing IITA's breeding modernization agenda and strengthening its leadership in data‑driven crop improvement.
In line with this, we invite suitably qualified and experienced individuals to express their interest in providing the required services.


Scope of Work / Key Responsibilities
The consultant will be responsible for, but not limited to, the following:


Develop and validate robust quantitative genetic models for yam improvement.
Enhance genomic selection accuracy and optimize deployment strategies within the breeding pipeline.
Build internal capacity of scientists, technicians, and students through targeted training and mentorship in quantitative genetics.
Deliver validated quantitative genetic models to address priority yam traits.
Produce a comparative report on genomic selection models and associated prediction accuracies.
Design and facilitate training sessions or workshops, including preparation of all training materials.
Develop reproducible R or Python analytical workflows and document Standard Operating Procedures (SOPs) for long‑term use.
Prepare a comprehensive technical report with actionable recommendations for breeding optimization.
Support the GPCP, OCS, and selection index development, and develop standard operating procedures (SOPs) and reusable scripts.
At least one scientific manuscript developed and submitted to a high-impact journal.


Requirements
Required Qualifications and Experience:


Ph.D. in Quantitative Genetics, Statistical Genetics, Plant Breeding, or a closely related discipline.
Minimum of seven (7) years of relevant professional experience applying quantitative genetic principles within crop improvement programs.
Advanced proficiency in R is mandatory, with additional experience using ASReml, BGLR, Sommer, and AlphaSimR considered an asset.
Demonstrated expertise in genomic prediction and mixed‑model methodologies, including additive, dominance, epistatic, and genotype‑by‑environment interaction models.
Proven hands‑on experience with ASReml/ASReml‑R, particularly in fitting and troubleshooting complex linear mixed models, estimating variance components, and resolving model convergence challenges.
Strong programming skills in R, including use of packages such as asreml, sommer, BGLR, rrBLUP, lme4, and caret, complemented by experience in Python for data management, machine learning, or workflow development.
Experience implementing GBLUP, PBLUP, Bayesian methods, RKHS, Random Forest, and other genomic selection approaches.
Prior experience working with large‑scale genotypic, phenotypic, and multi‑environment trial datasets, and supporting breeding teams in interpreting model outputs for decision‑making is highly desirable.


Duration and Location of the Assignment:


The consultancy is expected to last for a period of Six (6) months. The assignment will be based Ibadan & Abuja.