SUPPORT
Are you working on an exciting AI-related project and need support to take it further? Are you looking for financial assistance to attend MICCAI?
AFRICAI is committed to empowering African researchers and practitioners in the field of AI for healthcare. As part of our mission, we provide various forms of support to our members. At the moment, there are no open calls.
Please check our website and LinkedIn for updates or contact us directly.
Grants, awards, mentoring, writing assistance
Award for Best AFRICAI Journal Club
This award (€150,- EUR) will be given to the best AFRICAI Journal Club.
Mentorship Awards
The AFRICAI Mentorship Awards (€300 EUR/month) support 10 African students pursuing AI research through dedicated mentorship and prepare their paper submission and visit to MICCAI 2026.
Travel Awards
Travel grants (€4000 EUR) to support 5 African students or researchers with a Travel Grant to attend the MIRASOL workshop at MICCAI 2025 in Daejeon, South Korea on Saturday, September 27th 2025.
Mentorship Awards
Below you will see the selected candidates for the ten AFRICAI Mentorship Awards 2025.
Jill Sunday
Jill Sunday is a 5th year BSc Biomedical Engineering student at the Technical University of Mombasa, Kenya. Her research focuses on applying AI to medical imaging, with a publication at MICCAI Meets Africa 2024 and recognition as a recipient of the AFRICAI Mentorship Award 2025. She is passionate about developing hospital-ready AI solutions tailored to African healthcare needs.
Mariam Tamer
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Khaoula Alaoui Belghiti
Khaoula Alaoui Belghiti; a PhD student at the School of Information Sciences (ESI), Morocco. My research focuses on computer vision and deep learning applications in healthcare, particularly for multiple sclerosis diagnosis and progression analysis, exploring efficient methods for MRI generation and lesion segmentation. Beyond research, I'm active in AI communities and initiatives that promote AI presence and accessible technology in Morocco.
Toufiq Musah
Toufiq Musah is a Biomedical Researcher & Engineer with interests in artificial intelligence applications in healthcare, developing trustworthy AI tools that address challenges in both low-resource and advanced clinical settings. His recent work spans brain tumor segmentation, uncertainty estimation in medical imaging, and pathological complete response assessment in breast tumor treatment.
Blessing Olorunfemi
Blessing Olorunfemi is a PhD student in Computer Science at Redeemer’s University. His research focuses on developing machine learning and deep learning models for healthcare applications, particularly in the early detection of diabetes, heart disease, and breast cancer. He has designed application-oriented models to support diagnosis and prediction, and is currently developing an innovative hybrid Vision Transformer–CNN framework designed to enhance early and precise breast cancer detection and prediction.
Kangwa E. Mukuka
Kangwa Mukuka is an undergraduate researcher in the Department of Computing and Informatics at the University of Zambia. His research interests include Machine Learning and the application of Artificial Intelligence in Healthcare. Currently, he is pursuing a BSc in Computer Science majoring in Computer Systems Engineering. He is passionate about developing impactful solutions aimed at tackling the current challenges faced by healthcare professionals in Zambia, with the goal of optimising these processes to save lives.
Bernes Lorier Atabonfack
Bernes L. Atabonfack is an Artificial Intelligence (AI) researcher and engineer with a strong focus on medical imaging, deep learning, and applied AI in healthcare. He holds a Master of Science in Artificial Intelligence Engineering from Carnegie Mellon University Africa and a Bachelor of Technology in Software Engineering with a major in Data Science. His work bridges the fields of AI research, biomedical engineering, and real-world applications, aiming to improve global healthcare solutions.
El Mohamadi Houda
Houda El Mohamadi is a PhD candidate in Artificial Intelligence at Mohammed V University in Rabat, Morocco, in cotutelle with the University of Orléans, France. Her research focuses on developing explainable and efficient knowledge distillation frameworks for medical image classification in low-data. She is particularly interested in integrating vision–language models and semantic guidance to improve interpretability, robustness, and clinical relevance of AI systems for healthcare applications.
Cheikh Yakhoub Maas
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Aondona Iorumbur
Aondona Moses is an MSc Medical Physics student at the University of Lagos, Nigeria. His research focuses on medical imaging and artificial intelligence, particularly the development of state-of-the-art models for brain tumor segmentation and MRI analysis. With a background in physics and a strong interest in deep learning, he is dedicated to advancing AI-driven medical imaging solutions that can enhance diagnosis and treatment in low-resource healthcare settings.