Programme Thesis
This programme funds a one-year postdoctoral fellowship at AIMS South Africa, linked to a one-year Eric and Wendy Schmidt AI in Science Fellowship at Imperial College London, to support an excellent researcher in using AI to advance a specific area of science or engineering. It exists to accelerate AI-driven scientific discovery, particularly in sub-Saharan Africa, by providing a unique UK-South Africa research experience and fostering leadership in the AI-for-science community.
Selection Criteria
- Hold a PhD (or equivalent) in an appropriate discipline by 1 September 2027.
- Potential for leadership qualities, e.g., initiative on research projects.
- Outstanding research record commensurate with experience (thesis, publications, conference presentations, code).
- Research proposal within the AI in Science remit (AI broadly interpreted; science includes natural science and engineering, epidemiology, biology, basic biomedicine; excludes clinical medical themes and conventional medical imaging).
- Proposal must be transformative for a particular area of science, not generic AI.
- Application must include: full CV, 1-page publication elaboration, 1-page research proposal summary, 3-page research proposal, and ¼-page kindness statement.
- Must apply to both AIMS and Imperial College fellowships.
Past Winners / Cohort Profiles
No specific past winners are listed on the page. However, typical profiles are postdoctoral researchers with strong quantitative backgrounds (e.g., physics, mathematics, computer science, engineering) who apply AI/ML to a specific scientific domain, often with a connection to Africa or a commitment to contributing to African research environments.
Ideal Candidate Fingerprint
The ideal applicant is a postdoctoral researcher with a PhD in a quantitative or scientific discipline, a proven track record of innovative AI-for-science research, and a clear, transformative research proposal that addresses a fundamental question in natural science or engineering. They demonstrate leadership potential, a collaborative spirit, and a commitment to fostering AI research in Africa.
Recommended Framing
Eniola's strongest angle is to leverage his TOPOLOGIX project, which uses ESM-2 protein language models and drug fingerprints to predict drug-resistance mutations from sequence alone, outperforming structure-based baselines. This directly fits the AI-in-Science remit (AI for biology/basic biomedicine, not clinical) and demonstrates transformative potential for drug resistance research, a critical global health issue. He should frame this as a fundamental scientific advance in molecular biology, not a clinical application, and emphasize its potential to accelerate drug development and combat antimicrobial resistance in Africa and globally.
Watch Out
Eligibility: Requires a PhD by 1 September 2027; Eniola is currently enrolled in an M.Sc. and does not hold a PhD, so he is not eligible unless he can complete a PhD by that date, which is unlikely. Also, his clinical pharmacology background may be perceived as clinical, but his research is computational and basic science, so he must clearly position it as non-clinical. Competitive disadvantage: He lacks a PhD and formal postdoctoral experience, which are core requirements.
Research History
2026-08-04 19:59 · high confidence
2026-07-30 09:02 · high confidence