The Infectious Disease Epidemiology lab of Prof. Danesh Moradigaravand is looking for student interns to work on a range of projects on the intersection of machine learning and microbial genomics. We are interested in understanding the evolution and epidemiology of antimicrobial resistance strains recovered from natural sources, including clinical and environmental sites. Antimicrobial resistance is a global health threat, expected to become a leading cause of deaths worldwide within the next three decades. This is predominately due to the rapid emergence of novel genetic variants, which lead to new resistance mechanisms. The understanding of the genetic repertoire of resistance is henceforth important to design new antimicrobial strategies and develop new compounds. In this project, the student will develop a machine learning-based predictive model to systematically identify resistance determinants in bacterial genomes. The projects involves integration of genomic and phenomic data, generated by high throughput phenotypic assays using an off-the-shelf machine learning method. The student will implement an entire predictive pipeline and deploy the model as a data science solution. The project helps student gain hands-on experience of programming, next generation sequencing data analysis and machine learning