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Application Deadline: April 21, 2023
Academic Hive for African Scholars is pleased to announce the call for applications for the 2023 Summer Institute in Computational Social Science, Calabar (SICSS-Calabar) in Nigeria. The Summer Institute aims to bring together graduate students, postdoctoral researchers, and early-career faculty who are interested in computational social science and data science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived).
Participants will have the opportunity to attend lectures, participate in group problem sets, and work on research projects. The Summer Institute will also feature talks from external speakers who conduct computational social science research in academia, industry, and government.
The program will cover topics such as text as data, website scraping, digital field experiments, machine learning, and ethics. There will be ample opportunities for students to discuss ideas and research with the organizers, other participants, and visiting speakers.
Starting Date: June 26- July 07, 2023
Duration: 02 Weeks (in-person)
Eligibility:
Applicants from all backgrounds and fields of study are welcome to apply to SICSS-Calabar, participation is however restricted to:
- Applicants resident in South-South and South East regions of Nigeria (Cross River, Akwa-Ibom, Bayelsa, Delta, Edo, Rivers, Abia, Anambra, Ebonyi, Enugu, and Imo states)
- Applicants should have basic knowledge of R for data science (if you don’t have this, you can take this free short course to learn about R for data science before you apply)
- Postgraduate students (PGD, MSc, Ph.D.),
- Early-career University Faculty, and
- Industry Professionals with an interest in applying data science techniques to addressing pressing social problems.
Selection Criteria:
Applicants for the SICSS-Calabar will be selected based on the following criteria:
- Research and teaching in the area of computational social science
- Contributions to public goods, such as creating open-source software, curating public datasets, and creating educational opportunities for others
- Likelihood to benefit from participation
- Likelihood to contribute to the educational experience of other participants
- Potential to spread computational social science to new intellectual communities and areas of research.
