PhD Student – Plant Breeding, Genomics, Phenomics and AI

University of Guelph, Ontario Agricultural College; Guelph; On-site; CAD 26,000 - 36,000; Full time

Job summary

The Dry Bean Breeding & Computational Biology Program at the University of Guelph invites applications for a full-time PhD position integrating plant breeding, genomics, phenomics, artificial intelligence, and disease resistance research in adzuki bean. Adzuki bean is a high-value pulse crop of growing importance in Ontario and international markets.

The project will use advanced molecular, imaging, field, and computational approaches to accelerate the development of resistant adzuki bean

Qualifications

• A master’s degree in plant breeding, genetics, plant science, molecular biology, bioinformatics, data science, plant pathology, agriculture, or a closely related field.
• Strong interest in pulse crop improvement, disease resistance, genomics, phenomics, and applied plant breeding.
• Experience or interest in field, greenhouse, growth-chamber, and laboratory research.
• Strong organizational skills, attention to detail, and the ability to work effectively in an interdisciplinary team.
• Excellent written and oral communication skills.
• Motivation to conduct and publish high-quality scientific research.

Responsibilities

• Assemble and phenotype diverse adzuki bean germplasm for disease resistance and agronomic traits.
• Conduct greenhouse and controlled-environment screening for soybean cyst nematode.
• Establish and evaluate Ontario field trials, including bacterial blight nurseries and replicated phenotyping trials.
• Collect high-throughput phenotyping data using RGB, multispectral, hyperspectral, camera-based, and drone-based imaging.
• Support DNA extraction, genotyping-by-sequencing, SNP discovery, and quality-control workflows.
• Conduct genome-wide association studies, population structure analysis, candidate gene identification, and functional annotation.
• Develop phenomic indicators and AI-supported models for early disease detection and resistance classification.
• Integrate genomic markers, phenomic indicators, disease scores, and agronomic data to support marker- and image-assisted selection.
• Prepare manuscripts, conference presentations, progress reports, and knowledge-mobilization materials.

Skills

Pulse crop improvement

Genomics

Phenomics

Applied plant breeding

Field research

Interdisciplinary teamwork

Scientific writing

Communication skills

Education

Master's degree in plant breeding / genetics

Tools

R

Python

GWAS software

Bioinformatics tools

Job description

The Dry Bean Breeding & Computational Biology Program at the University of Guelph invites applications for a full-time PhD position integrating plant breeding, genomics, phenomics, artificial intelligence, and disease resistance research in adzuki bean.

Adzuki bean is a high-value pulse crop of growing importance in Ontario and international markets. However, soybean cyst nematode and bacterial blights are emerging threats to its yield, seed quality, and long-term production stability. This project will use advanced molecular, imaging, field, and computational approaches to accelerate the development of resistant adzuki bean germplasm and practical selection tools for breeding.

Key research activities

• Assemble and phenotype diverse adzuki bean germplasm for disease resistance and agronomic traits.
• Conduct greenhouse and controlled-environment screening for soybean cyst nematode.
• Establish and evaluate Ontario field trials, including bacterial blight nurseries and replicated phenotyping trials.
• Collect high-throughput phenotyping data using RGB, multispectral, hyperspectral, camera-based, and drone-based imaging.
• Support DNA extraction, genotyping-by-sequencing, SNP discovery, and quality-control workflows.
• Conduct genome-wide association studies, population structure analysis, candidate gene identification, and functional annotation.
• Develop phenomic indicators and AI-supported models for early disease detection and resistance classification.
• Integrate genomic markers, phenomic indicators, disease scores, and agronomic data to support marker- and image-assisted selection.
• Prepare manuscripts, conference presentations, progress reports, and knowledge-mobilization materials.

Training environment

The successful candidate will join an interdisciplinary and applied research environment within the Department of Plant Agriculture at the University of Guelph. The student will work with collaborators in plant breeding, remote sensing, machine learning, plant pathology, molecular genetics, bioinformatics, and pulse crop production.

The position provides training in modern plant breeding, disease phenotyping, genomics, high-throughput phenotyping, AI-based analytics, scientific writing, and research communication. The student will also have opportunities to participate in field days, industry meetings, extension activities, collaborative projects, and peer-reviewed publications.

Required qualifications

• A master’s degree in plant breeding, genetics, plant science, molecular biology, bioinformatics, data science, plant pathology, agriculture, or a closely related field.
• A strong interest in pulse crop improvement, disease resistance, genomics, phenomics, and applied plant breeding.
• Experience or interest in field, greenhouse, growth-chamber, and laboratory research.
• Strong organizational skills, attention to detail, and the ability to work effectively in an interdisciplinary team.
• Excellent written and oral communication skills.
• Motivation to conduct and publish high-quality scientific research.

Assets

Experience with one or more of the following would be an asset:

• R or Python
• Statistical or image analysis
• GIS and remote sensing
• Sequencing data and bioinformatics
• GWAS and SNP marker analysis
• Genomic prediction
• Machine learning
• High-throughput phenotyping

The University of Guelph is committed to equity, diversity, and inclusion. Applications from individuals from underrepresented groups are strongly encouraged.

• Learn more about our program: https://uogbeans.com/