We develop Bayesian inference software for practical population biologists. This software is based on Markov chain Monte Carlo methods using coalescence theory. These methods use genomic data to analyze potentially complex population models used in biogeography or epidemiology. Research opportunities include algorithm development and improvements, probabilistic model comparison techniques, simulation testing the methods, and analysis of real data.
Our group provides training in population genetics, in particular coalescence theory and software development. We will also provide mentoring opportunities including proposal writing, manuscript writing, and presentation preparation.