1. A MSc/PhD Position in development of genetic evaluation systems
Project information
- This project, supported by an NSERC Alliance grant, focuses on developing and implementing the genetic evaluation in mink. The research aims to collect key phenotypic data in different farms in North America to derive the breeding values and develop genetic evaluation systems in mink.
Project goals
- Development and implementation of genetic evaluation systems in mink, using large-scale phenotypic and genomic data.
- Collection and integration of key phenotypic records from North American mink farms to derive breeding values.
- Contribution to breeding program design for improved robustness, health, and productivity in mink populations.
Requirements
- Bachelor's/master's degree in quantitative genetics, animal science, computational biology, or closely related fields.
- Proficiency in BLUP and related statistical analyses.
- Familiarity with BLUP F90+, ASreml, or other genetic evaluation software.
- Highly motivated, with a collaborative and creative spirit.
- Strong communication skills for teamwork, industry collaboration, and dissemination of research findings.
Time
- Start Date: February 2026; Duration: 3 years (full-time MSc/PhD program)
- Stipend: $35,000 per year (for three years)
· For admission into the graduate program, the candidate must be admissible to the Dalhousie Agriculture, graduate program: https://www.dal.ca/faculty/gradstudies/graduate-programs-admissions/admission_requirements.html
Applicants must submit a cover letter stating qualifications and experience, a Curriculum Vitae, including a list of publications and major projects, and the names and contact information of three referees to Dr. Younes Miar, email: miar@dal.ca and Dr. Duy Do, email: duy.do@dal.ca.
2. A MSc position in improving mink robustness traits using machine learning and whole-genome sequence data
Project information
- This project supported by NSERC focuses on applying statistical methods and machine learning to improve the robustness and health traits in mink. The whole genome sequence data, low-pass sequencing, as well as phenotypic data will be used for analyses.
Project goals
- Development and application of genetic/genomic evaluation methods for mink robustness traits.
- Integration of machine learning algorithms (e.g., predictive modeling, feature selection) with quantitative genetics.
- Contribution to breeding program design for improved resilience and productivity in mink populations.
Requirements
- Bachelor’s degree in animal science, animal genetics, biology, or closely related fields with a strong foundation in quantitative genetics and genomics.
- Knowledge of or willingness to learn R and Python and familiarity with machine learning concepts (classification, regression, and neural networks).
- Highly motivated, with a collaborative and creative spirit and strong communication skills for teamwork and dissemination of research findings.
Time
- Start Date: May 2026, Duration: 2 years (full-time MSc program)
- Stipend: $27,000 per year (for two years)
For admission into the graduate program, the candidate must be admissible to the Dalhousie Agriculture, graduate program:
https://www.dal.ca/faculty/gradstudies/graduate-programs-admissions/admission_requirements.html
Applicants must submit a cover letter stating qualifications and experience, a Curriculum Vitae, including a list of publications and major projects, and the names and contact information of three referees to Dr. Younes Miar, email: miar@dal.ca and Dr. Duy Do, email: duy.do@dal.ca
3. A PhD Position in AI and Genomics for Resilience Traits in Farm Animals
Project information
- This project supported by NSERC focuses on applying artificial intelligence (AI), statistical methods, and advanced genomic analyses to improve resilience traits in farm animals. The research aims to better understand the biology underlying complex traits such as robustness, disease resistance, and adaptability, and to translate these findings into practical applications for the livestock industry. The project will leverage large-scale genomic datasets, phenotypic records, and high-performance computing resources to develop innovative approaches for genetic evaluation and trait prediction. Students will work closely with collaborators at Dalhousie University, industry partners, and international research centers.
Project goals
- Development and application of AI-driven genomic evaluation methods for resilience traits in farm animals.
- Integration of machine learning algorithms (e.g., deep learning, predictive modeling, and feature selection) with population genetics and quantitative genomics.
- Contribution to breeding program design for improved sustainability and productivity in livestock populations.
Requirements
- Master’s degree in quantitative genetics, animal science, computational biology, or closely related fields.
- Proficiency in or willingness to learn R and Python.
- Familiarity with AI and machine learning concepts (classification, regression, neural networks, deep learning).
- Experience with bioinformatics tools and high-performance computing environments is an asset.
- Highly motivated, with a collaborative and creative spirit. Strong communication skills for teamwork, industry collaboration, and dissemination of research findings.
Time
- Start Date: September 2026; Duration: 4 years (full-time PhD program)
- Stipend: $35,000 per year (for 4 years)
· For admission into the graduate program, the candidate must be admissible to the Dalhousie Agriculture, graduate program: https://www.dal.ca/faculty/gradstudies/graduate-programs-admissions/admission_requirements.html
Applicants must submit a cover letter stating qualifications and experience, a Curriculum Vitae, including a list of publications and major projects, and the names and contact information of three referees to Dr. Younes Miar, email: miar@dal.ca and Dr. Duy Do, email: duy.do@dal.ca.