Applications for summer 2025 are CLOSED.
The Center for Computational Thinking’s Ph.D. Computational Fellowship provides Ph.D. students in any Ph.D. program who lack summer funding with foundational classes in data science to accelerate their own research program.
The fellowship provides students with project- and team-based learning to better prepare them for data-centric research problems, and aims to create a pipeline of diverse, curious, and savvy individuals who will be future leaders in computation. Through this program, students continuously develop their emerging programming skills to apply to academic centered research questions.
During this summer-long fellowship, students apply their new computational skills to research problems relevant to their fields, from the humanities to the social sciences to the natural/physical sciences. At the conclusion of the summer, the students prepare a short presentation and record of their project to share broadly.
The fellowship consists of two parts:
- A weeklong “boot camp” that will provide students with a zero-entry introduction to the R programming language.
- Weekly office hours with assigned mentors.
Introduction to Data Science Boot Camp
This workshop introduces students to the emerging field of data science, including data analysis and visualization. Students will be provided with datasets and introduced to packages and code used to examine data. In the first half of each class, students will be lectured on methods and shown demonstrations; in the second half, students will use tools to analyze real data; laptop computers are required.
Methods for filtering, sorting, and transforming data will be discussed, along with visualization tools and options. Particular attention will be paid to code interpretation and data provenance methods by learning to generate reproducible data output files. For a final bootcamp project, students will present their research question and exploratory data analysis, and share findings with the cohort in a short oral presentation. Although specific pedagogical datasets will be used for analysis in class, this workshop will provide broadly applicable tools to reproducibly analyze and visualize data across domains.
For more information, contact computationalthinking@duke.edu.
Program Details
Who is eligible?
- Duke Ph.D. students from any department
- Preference will be given to applicants who have completed their prelims and have at least 1-2 years to graduate.
What is the stipend? $8,755; paid in monthly installments
What is needed to apply?
- Up-to-date CV (maximum 2 pages)
- One-page description of your computational background, reason for applying, and the activities you propose to perform during the fellowship.
When is it?
- June 2 - Aug. 22 (12 weeks), beginning with a weeklong data science boot camp
Where is it? Hybrid, with on site participation at the Duke Marine Lab in Beaufort, NC and Duke's West Campus (depending on field of study).
Who leads the program? Akshay Bareja, D. Phil., Assistant Professor of Medicine
What's the application deadline? March 31, 2025.
Fellows' Project Videos
Several fellows have recorded videos featuring their projects and how they applied computational methods to their research. See the YouTube Playlist.
Rafid Shidqi, Ph.D. Computational Fellow
After participating in the Computational Fellowship program in summer 2024, Rafid Shidqi published the manuscript he had been working on during the fellowship in Frontiers Journal in Ocean Sustainability, Marine Governance section. The paper is available for download. Per Rafid, "...[T]he fellowship has been very meaningful!"
Emily Melvin, Ph.D. Computational Fellow and Mentor
Ph.D. candidate Emily Melvin participated as a student in the Computational Fellowship program in summer 2023 and returned as a mentor in summer 2024. She posted on LinkedIn about two datasets she and her team at the Digital Oceans Governance Lab had recently published, and expressed gratitude to the CCT and the program's leader, Akshay Bareja, for helping her build the skills to complete her project. Watch Emily explain her project in this video and check out one of the data sets here.