Programme Thesis
The AI2050 Fellows program, funded by Schmidt Sciences, supports researchers who address the defining question: 'It's 2050, and AI has turned out to be hugely beneficial to society — what happened?' It funds both senior and early-career investigators to tackle 10 'hard problems' in AI, aiming to advance AI's capacity to benefit humanity across science, technology, and risk mitigation.
Selection Criteria
- Early Career Fellows: postdoctoral or pre-tenure researchers (from around the world).
- Senior Fellows: established leaders with significant contributions.
- Research must address one or more of the 10 'hard problems' (e.g., major scientific questions, technical issues, risks).
- Potential for AI to be hugely beneficial to society by 2050.
- Track record of research excellence (publications, awards, citations).
- Likely evaluation criteria: novelty, feasibility, interdisciplinary approach, societal impact, alignment with AI2050 mission.
- No explicit eligibility restrictions on nationality or affiliation, but early-career status is required.
Past Winners / Cohort Profiles
Past cohorts include 99 fellows across 8 countries and 42 institutions. Examples from 2025: Early Career Fellows are mostly assistant professors at top universities (Harvard, Stanford, MIT, EPFL, etc.) or research fellows at Oxford. Senior Fellows are prominent professors (e.g., Surya Ganguli, Dawn Song). Profiles are highly accomplished AI researchers with strong publication records and institutional backing. Some fellows work on AI for science (e.g., AlphaFold integration), AI safety, and societal benefit.
Ideal Candidate Fingerprint
The ideal applicant is a postdoctoral or pre-tenure researcher at a leading institution with a strong publication record in AI or AI-adjacent fields, working on a high-impact problem that aligns with AI2050's 'hard problems.' They demonstrate interdisciplinary thinking, a clear vision for AI's societal benefit by 2050, and have a track record of recognized contributions (papers, awards).
Recommended Framing
Eniola's strongest angle is to position the CCT model as an AI-driven framework for addiction neuroscience, directly addressing the hard problem of AI for health and societal benefit. Emphasize how Bayesian MCMC calibration and dynamical systems modeling represent a novel AI approach to a pressing global health issue, and how AI2050's support could scale this work toward real-world impact by 2050. Highlight the interdisciplinary expertise (pharmacy, computational modeling, software engineering) and the Africa/Nigeria angle to differentiate from typical academic applicants.
Watch Out
Eligibility: Early Career Fellows are typically postdoctoral or pre-tenure researchers; Eniola is currently enrolled in a Master's program and is an independent researcher, which may not meet the 'postdoctoral or pre-tenure' requirement. Competitive disadvantage: Lack of a PhD and formal academic appointment may be a significant barrier, as past winners are predominantly faculty at top universities. Also, the program is highly competitive with a low acceptance rate.