(1) Data science approaches to ecological forecasting and decision making

Closed. This professor is continuing with Fall 2019 apprentices on this project; no new apprentices needed for Spring 2020.

Become familiar with the mathematical, statistical, and computational tools used in the group and learn how to apply these methods to answer questions in ecological research and conservation decision making.

Primary tasks will involve building a database from existing ecological timeseries that can be used to test and evaluate a range of ecological forecasting and machine learning methods in real time.

Qualifications: Familiarity with two or more of the following areas will make an applicant both more competitive and more likely to enjoy a successful and productive research experience: Introductory statistics and probability, dynamical systems/differential equations, programming (particularly programming and data analysis in R), familiarity with git/GitHub, courses and/or research experience in ecology.

Weekly Hours: to be negotiated

(2) Data Science Software Development with Applications to Ecology and Environmental Science

Closed. This professor is continuing with Fall 2019 apprentices on this project; no new apprentices needed for Spring 2020.

Get involved on the front line of data science research in the R language with rOpenSci (https://ropensci.org).

Students will also be involved in the fundamentals of software development and maintenance, including unit testing and continuous integration. Successful completion of the project will involve peer review of software and a public software release of one or more R packages to the Central R Archive Network (CRAN).

Students will learn essential skills of data science and software development not usually taught in classes while working collaboratively with the rOpenSci team. Through this project, students will learn the following tools and technologies:

- GitHub: git flow, pull requests, GitHub API use
- R: Package development, documentation, testing
- JSON-LD: linked data principles, schema.org descriptions, parsing, serialization and validation
- Managing relational database interfaces with R (Postgres, MonetDB, etc)

Applicants will choose a specific software project in consultation and collaboration with Prof Boettiger. Currently active projects include ecological networks, taxonomic biodiversity tools, phylogenetics tools, structured and semantic data synthesis, global fish data, and ecological trait data.


Qualifications: Candidates should be self-motivated, curious, and able to collaborate effectively and professionally in an online environment. Applicants should have some prior experience with both R and GitHub, and an interest in applications to issues in ecology, environment and biodiversity.

Weekly Hours: to be negotiated

Related website: https://ropensci.org
Related website: https://carlboettiger.info