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.