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Open Positions

I am seeking strong, motivated students interested in algorithm development, algorithm implementation and engineering, and algorithm theory for complex problems or big data problems in bioinformatics and oceans. See here for more information about my research. I also sometimes have specific funding opportunities for machine learning projects with industry or government partners. If you are interested in joining my lab, please email me with a CV and cover letter or short statement of interests that clearly state your qualifications, any relevant publications, why you want to work with me and what kind of research you want to do. Specific positions may have additional application requirements as listed below.

Fall 2022 PhD Fellowships (2 available)

I have funding for up to two 4-year PhD Fellowships which will provide a $20,000 CAD stipend plus tuition. Phylogenetic “family trees” are a primary tool used for studying evolution such as the spread of antibiotic resistant bacteria or transmission of COVID-19. As we collect more and more sequencing data we need better algorithms to infer and compare phylogenetic trees. The successful applicant will design and implement new algorithms and data structures based on graph theory or statistical models for complex problems and big data challenges. The ideal candidate has a strong interest in algorithm design and analysis, graph theory, or bioinformatics.

Project area 1: Advanced algorithms for inferring evolutionary trees

We will develop and test a new class of phylogenetic inference algorithms based on direct exploration. Subprojects include integrating the prototype framework with marginal probability estimation and analyzing the results, developing techniques to sample or minimize repeated patterns called "phylogenetic terraces", and developing user-friendly open-source software for public use. See Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies and 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology for background information about these ideas.

Project area 2: Advanced algorithms for inferring lateral gene transfer

We will develop new algorithms and software for identifying and summarizing lateral gene transfer from a phylogenetics perspective. Fixed-parameter algorithms for the graph-theoretic maximum agreement forest problem have shown great promise in identifying candidate transfer events. New algorithms and ideas are needed to evaluate candidate scenarios efficiently from multiple agreement forests. See Supertrees Based on the Subtree Prune-and-Regraft Distance and Fixed-Parameter Algorithms for Maximum Agreement Forests for background information about these ideas.