The Morris Lab at the Donnelly Centre develops and applies cutting-edge deep learning models to interpret human cell epigenomic data and predict the effects of genetic variants. Although thousands of variants are linked to human traits and diseases, over 90% fall in noncoding regions of the genome, obscuring their function. By training deep learning models of cell type–specific activity, we are generating a comprehensive catalogue of predicted variant effects. The SUDS Scholar will help train and evaluate models using our high-performance computing cluster, and integrate predictions with in-house experimental datasets (e.g., CRISPR perturbation screens). By comparing variants with strong versus weak predicted effects, the student will assess whether these predictions can guide experimental design and identify likely functional variants. This project offers hands-on experience in computational genomics, machine learning, and collaborative research within a vibrant human genetics community.