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Call for Applications: Mak-AI Research & Innovation Mentorship Program
Cohort Duration: 2 Months (Full-time / Intensive Track)
Location: Makerere Centre for Artificial Intelligence (MAK-AI)
Nature of work: Hybrid (In-person lab access + remote execution)
Target Audience: Final-year Undergraduate and Graduate (Master’s) Students in Computing, Data Science, Engineering, Mathematics, and related domains.
Important dates
● Applications open: September 21, 2026 – Application deadline: September 26, 2026
● Technical interviews: September 28, 2026 – October 2, 2026
● Shortlisting &feedback: October 6, 2026
● Program commencement: October 12, 2026
● Program conclusion: December 11, 2026 (2 months duration)
Program Overview
The Makerere University Artificial Intelligence Lab (MAK AI) invites motivated undergraduate and graduate students to apply for our intensive 2 months Research & Innovation Mentorship Program.
This initiative bridges the gap between theoretical machine learning and real-world African impact. Participants will work directly with research scientists, software engineers to tackle pressing societal challenges in sectors such as agriculture, public health, and language. Fellows will leverage high-value local datasets collected, curated, and validated directly by MAK AI research team to build deployable models, tools, and production-ready pipelines.
Core Focus Areas & Local Datasets
The mentees will be assigned to targeted research tracks utilizing MAK AI’s curated repositories:
- Agricultural & Crop Disease Surveillance: Utilizing localized crop imagery datasets for real-time mobile disease diagnostics and yield prediction.
- Health & Clinical Decision Support: Developing multimodal models and computer vision pipelines using clinical, diagnostic, and microscopy imaging datasets.
- Natural Language Processing (NLP) for Low-Resource Languages: Developing models, translation pipelines, speech-to-text tools, and text analytics tailored to local East-African languages using crowdsourced audio, dialect corpora, and local multilingual text datasets.
What the Program Offers
- Hands-on Mentorship: Weekly technical sessions and one-on-one advising from MAK AI researchers.
- Compute & Infrastructure Access: Access to lab compute resources, GPU instances, and structured local dataset repositories.
- End-to-End Tool Development: Mentorship covering data preprocessing, model development, edge optimization, and deployment
- Publications: Support for peer-reviewed academic publication.
- Retention & Growth Opportunities: 4 Top-performing mentees who demonstrate exceptional technical excellence, leadership, and project execution will be considered for retention as research assistants or full-time project developers.
- Certificate of Completion: Official recognition from MAK AI Lab highlighting validated technical contributions.
Eligibility Criteria
- Currently enrolled as an upper-level undergraduate (3rd/4th year) or graduate student (MSc) in Computer Science, Software Engineering, Data Science, Information Systems Technology, Electrical Engineering, Statistics, Health informatics or a closely related computational field.
- Demonstrated competency in Python and fundamental data science libraries (e.g., NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow).
- Familiarity with foundational machine learning and deep learning paradigms.
- Strong analytical problem-solving skills and a proven commitment to leveraging data for local societal impact.
- Ability to commit 15–20 hours per week across the 2 month period.
Application Requirements
Interested applicants must submit the following:
- Curriculum Vitae (CV): Highlighting relevant academic background, programming experience, and any previous projects (include GitHub or portfolio links if available).
- Statement of Intent (Max 1 Page): Outlining your technical background, interest in MAK AI’s local datasets, and the specific societal domain you wish to address.
- Academic Transcripts: Latest unofficial transcripts or proof of current university enrollment.
- Code Sample/Technical Portfolio: A link to a GitHub repository, Kaggle notebook, or technical project demonstrating your coding proficiency.