I have two topics that I really want to get involved in.
1) Confirming current research in chromosome structure while using machine learning to find relationships in genetic motifs. For instance, is function associated with how the genome condenses?
2) Developing novel Computer Vision algorithms to learn from small sample sizes and video using specialized equipment.
Maybe there is a common problem between 1) and 2) that I can study in depth, but these seem like very different projects.
Would it be feasible to apply for a Master's in Quantitative Genetics (+ Research Thesis) with electives in Machine Learning and Statistics, then apply for a PhD in Machine Learning? I have a double major in Math and Computer Science with a few electives in genetics.