Dr. Peter Beerli

Dr. Peter Beerli
Stem Field
Scientific Computing
Title of Research
Computational population genetics
Description of Research Area

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.

Special Research & Career Skills

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.