MOTIVATION LETTER ARUA Early-career Research Fellowships 2026 The CCT model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, has confirmed all five pre-registered hypotheses through Bayesian MCMC calibration of a 14-parameter ODE system. Three sole-authored preprints are under review at peer-reviewed journals: IART, PNPBP, and NBR. This project is data-rich, computationally complete, and ready for the six-month residency the ARUA fellowship requires. I am an independent computational researcher based in Nigeria, holding a B.Pharm from the University of Ibadan and currently enrolled in the M.Sc. Digital Health programme at Hasso Plattner Institute, Potsdam. The CCT model sits at the intersection of addiction neuroscience and computational pharmacology, aligning directly with ARUA’s neuroscience or pharmacology Centres of Excellence. Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the work meets international standards. The fellowship would fund a six-month placement at a host CoE, where I would fit the CCT model to real behavioral data, completing the circuit-layer parameter estimation that is currently labeled illustrative. I have pre-existing contact with potential supervisors at the University of Cape Town’s Neuroscience Institute and the University of Ibadan’s Centre for Drug Discovery, Development and Production. The programme’s requirement that applicants hold a PhD is a known gap in my profile: I hold a B.Pharm and am pursuing an M.Sc., not a doctorate. I request clarification on whether equivalent professional qualifications or enrollment in a doctoral-track programme satisfies this condition. My track record, including a 1,847-record literature screen, confirmed hypotheses, and published co-authored work in Alcohol (Elsevier, under review), demonstrates a competitive early-career profile. The ARUA fellowship’s focus on building African research capacity matches my goal of establishing a computational neuroscience laboratory in Nigeria. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model addresses a specific gap in addiction pharmacology: no existing framework explains how three independent neural signals—dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast—combine to determine whether a reward-memory is encoded or suppressed. I formalized this as a three-axis ODE system solved with RK45, calibrated 14 free parameters using PyMC’s DEMetropolisZ sampler, and elicited priors from a systematic screen of 1,847 published records. All five pre-registered hypotheses (H1-H5) were confirmed. Posterior super-additivity ranged from 13 to 22 percentage points across model versions, meaning the combined effect of the three axes exceeds the sum of their individual contributions. This result has direct implications for designing combination pharmacotherapies that block memory reconsolidation in substance use disorder. The model is published as three sole-authored preprints on OSF and Zenodo, each under review. A co-authored paper is under review at Alcohol (Elsevier). During the ARUA fellowship, I will fit the circuit-layer parameters, currently labeled illustrative, to real behavioral data from rodent self-administration paradigms. The host CoE would provide access to these datasets and supervision for the behavioral fitting pipeline. The six-month timeline is feasible: the ODE solver, Bayesian calibration code, and literature database are already built. The deliverable is a fully calibrated, publication-ready model with behavioral validation, submitted to a high-impact neuroscience journal. This work also feeds into my broader platform, neurocascade, a receptor-to-behavior simulation engine that currently passes 62 of 62 tests across three calibrated receptor systems (mu-opioid, D2 dopamine, GABA-A). The ARUA fellowship would accelerate the transition from computational pharmacology to clinical application in African populations, where addiction treatment protocols remain understudied. SHORT ESSAY: RELEVANCE TO AFRICA Nigeria has fewer than 500 psychiatrists for a population exceeding 200 million. Substance use disorder treatment relies on protocols developed in Europe and North America, with no pharmacokinetic or pharmacodynamic data from African populations. The CCT model, once calibrated to behavioral data, can predict which combination of dopaminergic, glutamatergic, and affective-targeting drugs will block reward-memory reconsolidation in individual patients. This is directly relevant to ARUA’s mission of building research capacity that addresses African health challenges. My computational approach—ODE modeling, Bayesian calibration, and open-source code—is infrastructure-light: a laptop and access to a university HPC cluster are sufficient. I have already built four independent DuckDB-based ingest-to-analyze pipelines across life sciences, tech/AI, and social science domains, demonstrating the ability to operate without a dedicated laboratory. The ARUA fellowship would place this work within a Centre of Excellence that can provide the clinical datasets and supervisory expertise I currently lack as an independent researcher. The six-month residency would also allow me to train two to three graduate students in computational pharmacology methods, directly building the human capacity the programme prioritizes. SHORT ESSAY: METHODOLOGICAL APPROACH The CCT model uses a coupled ODE system with three axes: dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast. I solved the system with RK45 integration in Python (scipy). Bayesian calibration used PyMC’s DEMetropolisZ sampler with 14 free parameters. Priors were elicited from a systematic screen of 1,847 records from PubMed, PsycINFO, and Scopus, covering rodent and human studies of dopamine, NMDA, and affective manipulation in reward-memory paradigms. Posterior distributions confirmed all five pre-registered hypotheses. The model is implemented in a modular Python codebase with unit tests, version control on GitHub, and containerized execution for reproducibility. For the ARUA fellowship, I will extend the model to fit behavioral data from rodent self-administration paradigms. This requires adding a likelihood function that maps model output to observed lever-press or place-preference data, then re-running the MCMC calibration. The host CoE would provide the datasets and supervision for this step. The codebase is designed for this extension: the ODE solver and Bayesian engine are separate modules, and the data ingestion pipeline is already built. The six-month timeline is sufficient to complete the calibration, write the manuscript, and submit to a journal. CHECKLIST - [ ] Confirm ARUA eligibility regarding PhD requirement: contact programme officer to ask if B.Pharm plus M.Sc. enrollment qualifies, or if an exception is possible for independent researchers with published preprints and endorsements. - [ ] Identify specific ARUA Centre of Excellence or CoRE in neuroscience or pharmacology: contact University of Cape Town Neuroscience Institute and University of Ibadan Centre for Drug Discovery, Development and Production to confirm willingness to host. - [ ] Obtain letter of support from a potential supervisor at the host CoE. - [ ] Prepare concept note (max 1,500 words) describing the CCT model, the six-month workplan, and the deliverables. - [ ] Prepare cover letter specifying the chosen CoE and addressing visa requirements for a six-month stay in South Africa or Nigeria (depending on host). - [ ] Gather supporting documents: B.Pharm certificate, M.Sc. enrollment letter, ORCID profile, GitHub portfolio, three preprint DOIs, co-authored paper under review, endorsement emails from Berridge, Gershman, Daw, and Mattar. - [ ] Verify age requirement: applicant is 29, within the 35-year limit. - [ ] Verify post-PhD timeline: applicant does not hold a PhD, so the five-year post-PhD window is not applicable. Confirm whether this disqualifies the application or if an exception exists. EDITOR NOTES - Eligibility risk: The programme explicitly requires a PhD. The applicant holds a B.Pharm and is enrolled in an M.Sc. This is a critical red flag. The application should not proceed without first confirming with the programme officer whether an exception is possible. If no exception exists, this programme is not viable. - Age and timeline: The applicant is 29, within the 35-year limit. The post-PhD five-year window is not applicable because the applicant does not hold a PhD. This is another eligibility ambiguity that needs clarification. - Host CoE contact: The applicant has pre-existing contact with potential supervisors at UCT and UI, but this is stated in the profile without specific names or dates. The applicant must insert actual names, titles, and email correspondence records into the application. - Visa requirements: The cover letter must address visa requirements for a six-month stay. If the host is in South Africa, the applicant needs a research visa. If the host is in Nigeria, the applicant is a Nigerian citizen and does not need a visa. This must be specified.