Carl Boettiger, Professor

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

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

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 learning about and implementing methods and analysis through computational tools and computer programming. Students will also be expected to read the research literature and write scientific reports.

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

Related website: http://carlboettiger.info

Open (2) Data Science Software Development & Applications in Ecology and the Environment

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

Get involved on the front line of data science research in the R language with rOpenSci (https://ropensci.org). rOpenSci develops and maintains a wide ranging suite of software to access and manipulate heterogeneous scientific data sources.

This semester, student researchers will work closely with the faculty adviser and other members of the rOpenSci team to design and implement a JSON-LD based metadata model describing the data sources and the software packages used to access them. This system will improve search and discovery of relevant data and software.

Students will also be involved in the fundamentals of software maintenance and development.


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 & validation



Qualifications: Candidates should be self-motivated, curious, and able to collaborate effectively & professionally in an online environment. Prior experience with GitHub and some previous programming experience in any language are strongly recommended.

Weekly Hours: to be negotiated

Related website: https://ropensci.org