Applications are being accepted from properly qualified Africans interested in pursuing a career in data science for the Quantum Leap Africa PhD Scholarship in Data Science for the academic year 2023/24.
The AIMS doctoral training program will give emerging African scientists the chance to conduct cutting-edge data science research and work toward a Ph.D. degree within a high-quality training program in Africa, in collaboration with institutions around the world.
To be considered for the Quantum Leap Africa Ph.D. Scholarship, applicants must meet the following requirements:
- Master’s degree (completed by Sept 2023) in mathematics, statistics, computer science, engineering, physics or other relevant fields;
- Sufficient theoretical foundations evidenced by prior work (courses/thesis/other training);
- Qualification for pursuing research on the chosen topic, including relevant programming expertise;
- Research potential evidenced by academic performance and involvement in relevant academic activities;
- Motivation for pursuing a Ph.D. by research in the suggested topic;
- Being an African national.
- Fully Funded (Stipend, equipment, health insurance, relocation costs, conference attendance, direct cost to graduating institution such as tuition fees and registration fees.
- International supervisions teams from well-known research institutions.
- Application Form
- CV (you can use your own format, but please make sure to cover the content mentioned in this template that applies to your case).
- Transcripts (Please submit your undergraduate and your masters level transcripts. Additional transcripts can be submitted if relevant.)
How to Apply
Applicants for the Quantum Leap Africa PhD Scholarship 2023 must submit an online application by the application date. Applicants should be aware that the application procedure is divided into two stages. Phase 1 is the application and submission of all required papers, and Phase 2 is the invitation phase, in which successful applicants from Phase 1 are asked to round 2 of the selection process.