Programme Thesis
This programme aggregates fully funded PhD positions in computational neuroscience across multiple universities worldwide, providing stipends, tuition coverage, and research support for early-career researchers. It exists to connect talented applicants with funded doctoral opportunities in this interdisciplinary field, spanning neuroscience, data science, and computational methods.
Selection Criteria
- Academic excellence: strong prior degrees (e.g., GPA, class of degree) and relevant coursework.
- Research experience: demonstrated productivity in computational neuroscience or related fields (publications, preprints, code).
- Technical skills: proficiency in programming (Python, R), machine learning, dynamical systems, or neuroimaging.
- Fit with lab: alignment of research interests with specific faculty and projects at each university.
- Letters of recommendation: strong endorsements from established researchers.
- Motivation and potential: clear research questions, independence, and promise for future contributions.
- For some positions: specific field requirements (e.g., neuroscience, computer science, physics, mathematics).
Past Winners / Cohort Profiles
The page does not list past winners, but typical successful applicants are recent MSc graduates or early-career researchers with strong computational backgrounds, often holding publications or preprints, and with experience in machine learning, modeling, or data analysis. They usually have secured endorsements from known academics and demonstrate a clear research focus aligned with a lab's work.
Ideal Candidate Fingerprint
The ideal applicant is a highly motivated early-career researcher with a strong quantitative background (e.g., physics, computer science, mathematics, or computational biology), proven research output (publications or preprints), and technical proficiency in computational methods. They have a clear research vision that aligns with a specific lab's focus, and they bring a track record of independent, rigorous, and reproducible research.
Recommended Framing
For Eniola, the strongest angle is to leverage his CCT model as the centerpiece of his PhD applications, as it directly demonstrates his ability to integrate computational modeling, Bayesian inference, and neuroscience theory to address a fundamental question in addiction. His pre-registered hypotheses, confirmed results, and endorsements from leading neuroscientists (Berridge, Daw, Gershman) make him a standout candidate for labs focused on computational psychiatry or reward learning. He should explicitly frame his multi-domain skills (protein ML, dynamical systems) as complementary assets that can extend the CCT framework to novel predictions and collaborations.
Watch Out
The programme is a listing of multiple positions, so fit depends on specific labs; some positions may require a Master's degree (which Eniola is currently pursuing, not yet completed). His independent research profile may be viewed as unconventional compared to traditional academic trajectories, and he must ensure his application materials clearly articulate how his independent work translates to a PhD setting. Also, some positions have deadlines before his MSc completion (e.g., Aug 2026), so he must check each position's timeline.
Research History
2026-08-04 19:58 · medium confidence
2026-07-30 08:56 · medium confidence