MOTIVATION LETTER The CCT model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, addresses a specific mechanism by which addiction vulnerability is encoded and maintained. My work on this model, including Bayesian MCMC calibration with PyMC DEMetropolisZ across 14 free parameters and confirmation of all five pre-registered hypotheses, demonstrates a methodological rigor that directly serves the fellowship's theme of Inequity in Health. Addiction treatment outcomes in Nigeria and other LMICs are disproportionately poor, and computational frameworks that clarify the underlying neuropharmacology can inform more equitable intervention strategies. The fellowship's emphasis on interdisciplinary collaboration matches my research profile, which spans ODE modeling, protein-language-model embeddings, and circuit-level simulation. My TOPOLOGIX project, predicting drug-resistance mutations from sequence alone with an AUROC of 0.804 on the Platinum benchmark, covers 100% of mutations versus approximately 18% for structure-limited tools, a coverage gap that matters for settings where structural data is scarce. This is the same principle I apply to addiction research: methods that do not depend on expensive, infrastructure-heavy data collection are more transferable to resource-limited clinical environments. My independent research track record includes three sole-authored preprints currently under review at peer-reviewed journals, a co-authored paper under review at Alcohol (Elsevier), and a pre-registered replication study on hERG cardiotoxicity topology that settled a comparison the published literature had never actually run. The hERG study found topological features do not beat a plain descriptor baseline (AUROC 0.8426 versus 0.8782), a negative result I reported directly rather than reframed. This commitment to reporting what the data show, even when it contradicts the initial hypothesis, is the standard I would bring to the fellowship's joint activities and interdisciplinary collaborations. The 23-month residency requirement aligns with my enrollment in the M.Sc. Digital Health program at Hasso Plattner Institute, University of Potsdam, starting Winter Semester 2026/27. My current role as National Product Manager at Synthcare and my prior experience as a clinical pharmacist at Ramset Pharmacy ground my computational work in the practical realities of pharmaceutical care in Nigeria. The fellowship's support for researchers of all nationalities, combined with its focus on health inequity, offers a structure within which I can develop the CCT model into a tool that addresses the specific addiction treatment gaps I have observed in clinical practice. RESEARCH STATEMENT The Conjunctive Consolidation Threshold (CCT) model addresses a specific gap in addiction neuroscience: the field lacks a unified computational account of how reward-memory encoding can be prevented at the pharmacological level. My research program develops and validates this model across three coupled axes: dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast. The model is implemented as a system of ordinary differential equations solved with RK45, calibrated against literature-elicited priors from a 1,847-record screen, and validated through Bayesian MCMC with PyMC DEMetropolisZ. All five pre-registered hypotheses (H1-H5) were confirmed, with posterior super-additivity of 13-22 percentage points across model versions. The relevance to health inequity is specific. Addiction treatment outcomes in Nigeria are constrained by limited access to specialized psychiatric services, high medication costs, and a treatment gap that the World Health Organization estimates exceeds 80% for mental health conditions in LMICs. A computational model that identifies which pharmacological targets most effectively prevent reward-memory encoding can prioritize interventions that are affordable and implementable in these settings. The CCT model's tripartite framework suggests that combination approaches targeting multiple axes simultaneously may achieve super-additive effects, a finding with direct implications for treatment protocols that must work within severe resource constraints. My broader computational toolkit supports this research program. The neurocascade simulation engine couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics to behavioral-readout ODE layers, with 62 of 62 tests passing and Bayesian calibration across three receptor systems (mu-opioid, D2 dopamine, GABA-A). This engine allows me to test CCT predictions at the circuit level before clinical translation. The TOPOLOGIX project, using ESM-2 protein-language-model delta-embeddings and Morgan fingerprints with a Random Forest classifier, achieves an AUROC of 0.804 on the Platinum benchmark for drug-resistance mutations, demonstrating that sequence-based methods can replace structure-based approaches when structural data is unavailable, a common constraint in LMIC research settings. The ergofluids project, extending Koopman-operator methods with a Mori-Zwanzig memory kernel for drug-vehicle transport through tumor tissue, passed its synthetic-data gates but did not meet its primary pre-registered criterion on the first real-data gate. I reported this directly. This methodological honesty is central to my research identity: pre-registration, direct reporting of negative results, and explicit labeling of model parameters that remain illustrative pending real-data fits. During the fellowship, I will extend the CCT model in three directions. First, I will incorporate population-level variability in receptor expression and metabolic enzyme activity, using published pharmacogenomic data from African populations to parameterize the model for Nigerian clinical contexts. Second, I will develop a simplified clinical decision tool based on the model's predictions, designed for use by non-specialist health workers in primary care settings. Third, I will validate the model's circuit-level predictions using the neurocascade engine, testing whether the super-additive effects predicted at the molecular level propagate to behavioral readouts. The fellowship's emphasis on health inequity and interdisciplinary collaboration provides the structure needed to develop these extensions, and the host institute's research environment offers the computational and clinical expertise required for validation. SHORT-ANSWER ESSAY: INTERDISCIPLINARY COLLABORATION My research practice is built on explicit cross-disciplinary methods. The CCT model required integrating pharmacology (receptor binding kinetics, NMDAR physiology), computational neuroscience (dopaminergic reward prediction error, LTP dynamics), and statistical inference (Bayesian MCMC calibration). Each of these domains has its own literature, standards, and failure modes; the model's credibility depends on satisfying all three simultaneously. I have demonstrated this through concrete collaborations. Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU have endorsed my work. My co-authored paper under review at Alcohol (Elsevier) required coordinating experimental pharmacology findings with computational modeling across institutional boundaries. The hERG cardiotoxicity study required translating protein-ligand interface geometry into bipartite persistent homology features using Ripser and GUDHI, a method developed in algebraic topology, applied to a toxicology question, and evaluated against machine learning baselines. The fellowship's joint activities would benefit from my experience building shared research infrastructure. I have developed four independent DuckDB-based ingest-to-analyze pipelines across life sciences, technology, and social science domains, and I self-host local LLM serving with llama.cpp for on-demand model swapping. These are practical skills for interdisciplinary teams that need to share data and tools across methodological boundaries. For the fellowship's health inequity theme, interdisciplinary collaboration is not optional. Addiction outcomes are determined by neuropharmacology, health systems, economic constraints, and cultural factors. A model that addresses only the molecular level cannot reduce inequity; it must be connected to implementation realities. My clinical experience as a pharmacist at Ramset Pharmacy and my current role as National Product Manager at Synthcare provide the health-systems perspective needed to make this connection. I would bring this dual orientation, computational rigor plus clinical practice, to the fellowship cohort. SHORT-ANSWER ESSAY: TRACK RECORD AND INDEPENDENT RESEARCH My research output has been produced as an independent researcher, without institutional laboratory support. This has required building my own infrastructure: high-performance computing workflows using Nextflow and SLURM, Bayesian calibration pipelines in PyMC, and version-controlled repositories documented on GitHub. The results include three sole-authored preprints under review at peer-reviewed journals (IART, PNPBP, NBR), a co-authored paper under review at Alcohol (Elsevier), and a pre-registered replication study on hERG cardiotoxicity topology. The hERG study is representative of my approach. The published literature claimed topological features of protein-ligand interfaces could predict cardiotoxicity, but no one had run the comparison against a plain descriptor baseline. I ran it, pre-registered and powered, and found the topological features did not beat the baseline (AUROC 0.8426 versus 0.8782). This negative result is now the definitive answer to a question the field had been answering by assumption. Similarly, my interface-topology-for-resistance study found that the same topological constructs carry almost no signal for drug-resistance prediction (AUROC 0.425 and 0.485 on the Platinum benchmark), ruling out interface geometry as the driver and motivating the sequence-based TOPOLOGIX approach. The CCT model represents my most developed independent project. The model's 14 free parameters were calibrated against literature-elicited priors from a 1,847-record screen, and all five pre-registered hypotheses were confirmed. The posterior super-additivity of 13-22 percentage points across model versions indicates that combination pharmacological approaches may prevent reward-memory encoding more effectively than single-target interventions. This finding has direct implications for addiction treatment protocols in resource-limited settings. My academic record includes a B.Pharm from the University of Ibadan with a CGPA of 5.1/7.0, German equivalent 1.9, and I am enrolled in the M.Sc. Digital Health program at Hasso Plattner Institute, University of Potsdam, starting Winter Semester 2026/27. My employment history includes clinical pharmacy practice, research assistance in NMDA/insulin docking at CDDDP, and bioinformatics research in AMR genomics at GHRU-GSAR. This combination of clinical, computational, and research experience is the foundation for the independent research program I would bring to the fellowship. CHECKLIST - [ ] Verify current eligibility: confirm the programme does not require a doctoral degree at time of application, or confirm whether M.Sc. enrollment satisfies any equivalent qualification requirement - [ ] Confirm the 23-month residency requirement does not conflict with M.Sc. Digital Health coursework obligations at Hasso Plattner Institute, University of Potsdam - [ ] Verify the programme's host country and confirm the 12-month residency rule (not having resided in the host country for more than 12 months in the 2 years preceding the deadline) is satisfied - [ ] Obtain and attach ORCID record (0009-0001-9272-6735) and GitHub profile (github.com/AmunRaPtah) as evidence of research output - [ ] Prepare PDF copies of the three sole-authored preprints (CCT model, OSF/Zenodo) and the co-authored Alcohol (Elsevier) paper for upload - [ ] Prepare a one-page CV formatted to the programme's specifications, emphasizing independent research, pre-registered studies, and the CCT model - [ ] Draft a project timeline for the 23-month fellowship, mapping CCT model extensions (population-level parameterization, clinical decision tool, neurocascade validation) to specific months - [ ] Identify and contact two referees who can speak to computational modeling rigor and clinical pharmacology relevance; Kent Berridge, Samuel Gershman, Nathaniel Daw, or Marcelo Mattar are potential options - [ ] Confirm the programme's application portal requirements, including whether the motivation letter and research statement are separate uploads or combined - [ ] Verify the deadline is truly rolling and confirm the application review cycle timing relative to M.Sc. enrollment start date EDITOR NOTES - Eligibility risk: the selection criteria note a doctoral degree requirement, but the applicant profile indicates B.Pharm with M.Sc. enrollment. This must be verified against the programme's actual terms before submission; if the doctoral requirement is firm, this application should not proceed. - The CCT model was selected as the primary research line because its addiction neuroscience focus and computational methodology directly match the fellowship's health inequity theme. The hERG and TOPOLOGIX projects are presented as supporting evidence of methodological rigor, not as the main proposal. - The ergofluids project is described honestly: synthetic-data gates passed, first real-data gate did not meet its primary pre-registered criterion. No claim of validated IP or product-market fit is made for any project. - The applicant must insert personal details not in the profile: specific reasons for choosing this host country and institute, any prior connections to the host research environment, and how the 23-month residency will be financed beyond the fellowship salary. - The M.Sc. enrollment timing (Winter Semester 2026/27) versus the fellowship's 23-month duration and European location requires explicit conflict checking. The applicant must confirm coursework can be completed remotely or that the fellowship start date can be aligned with the academic calendar.