For Eniola, the strongest angle is to frame the CCT model and neurocascade engine as AI-driven computational neuroscience tools that directly address addiction—a critical but underfunded healthcare challenge in Africa. Emphasize how these models use Bayesian ML and dynamical systems to predict and prevent reward-memory encoding, offering a scalable, locally-relevant solution to substance-use disorders. This aligns perfectly with the fellowship's healthcare focus and showcases Eniola's unique multi-domain expertise.

Limited information on funding amount, deadline, and application materials reduces certainty. The program is new, so no track record exists. Eniola's work is highly computational and may be perceived as less directly 'African' unless explicitly tied to local healthcare challenges. Also, Eniola is currently enrolled in a German M.Sc. program, which might raise questions about long-term commitment to Africa.