Enhancing Sheep Production Efficiency through Advanced Techniques: A Focus on Machine Learning and Multiomics Data

This proposal is devoted to two areas of sheep production: animal health (parasitic infection) and feed efficiency, which both impact profitability and sustainability. We will investigate potential machine learning using biomarkers indicating parasite resistance and feed efficiency in differentiated animals early in life. One postdoctoral fellow (PDF) will use metabolomics, genomics, and machine learning to accomplish independent and collective objectives. PDF will find novel trends relating metabolites to gene expression and characterize predictive biomarkers for infection level and feed efficiency over time. Additionally, PDF will examine the relationship between feed efficiency and immune response and oversee MSc students’ omics data analysis. This project provides a great opportunity for the intern who is eager to work in the field, interact with industry partners, and implement advanced technology such as metabolomics, genomics, and machine learning.

Faculty Supervisor:

Ghader Manafiazar

Student:

Partner:

Purebred Sheep Breeders Association of Nova Scotia

Discipline:

Life Sciences

Sector:

Agriculture and Food; Life Sciences (not health); Technology

University:

Dalhousie University

Program: