MOTIVATION LETTER The CCT model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, emerged from a literature screen of 1,847 records and Bayesian MCMC calibration with 14 free parameters. All five pre-registered hypotheses were confirmed, with posterior super-additivity of 13 to 22 percentage points across model versions. This work, conducted without a PhD or institutional lab, produced three sole-authored preprints and a co-authored paper currently under review at Alcohol. The TOPOLOGIX pipeline, using ESM-2 protein-language-model delta-embeddings and Morgan fingerprints, predicts drug-resistance mutations from sequence alone at an AUROC of 0.804 on the Platinum benchmark, covering 100 percent of mutations versus approximately 18 percent for structure-limited tools. These results demonstrate that independent, pre-registered computational research at the intersection of pharmacology, neuroscience, and machine learning is possible from Nigeria, without a doctoral degree. The Women PhD Scholarships for International Students programme at ScholarshipBob directly addresses the structural barrier that prevents researchers like me from transitioning from independent work to formal doctoral training. I am enrolled in the M.Sc. Digital Health at Hasso Plattner Institute and University of Potsdam, starting winter semester 2026/27. This programme provides the computational and clinical foundations needed to extend my CCT model into a full-scale, behaviorally-calibrated simulation engine. The scholarship would fund the remaining gap between my current resources and the cost of completing this MSc and beginning a PhD that bridges computational pharmacology and digital health. Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm that my research trajectory is recognized at the highest level of computational neuroscience. My B.Pharm from the University of Ibadan with a CGPA of 5.1 out of 7.0, equivalent to a German 1.9, meets the academic merit criterion. I am a woman, a Nigerian national, and an international student seeking a PhD pathway. The programme's mission to empower women in science from low-income countries aligns exactly with my position: a female researcher from Africa who has already produced pre-registered, published work without institutional support. The scholarship would enable me to focus on extending the neurocascade simulation engine from its current 62-passing-tests state to a system that fits real behavioral data, and to deploy TOPOLOGIX as a freely available tool for predicting drug-resistance mutations in pathogens relevant to West Africa. These are concrete, measurable outcomes that serve both the scientific community and the public health needs of my home region. RESEARCH STATEMENT My research programme spans three interconnected domains: addiction neuroscience, protein-drug machine learning, and dynamical-systems methods for pharmacology. The unifying question is how to predict and prevent pathological outcomes at the molecular, circuit, and behavioral levels using computational models that are pre-registered, calibrated against empirical data, and openly reported regardless of outcome. The CCT model addresses a specific gap in addiction pharmacology: existing frameworks treat dopamine signaling, NMDA-receptor-dependent long-term potentiation, and affective contrast as separate mechanisms. The CCT model couples them into a single system of ordinary differential equations solved with RK45, with parameters estimated via Bayesian MCMC using PyMC's DEMetropolisZ sampler. The model predicts that simultaneous blockade of all three axes produces super-additive reduction in reward-memory encoding. All five pre-registered hypotheses were confirmed, with posterior super-additivity ranging from 13 to 22 percentage points across model versions. A co-authored paper is under review at Alcohol. The next step is to fit the circuit-layer parameters of the neurocascade engine to real behavioral data, which requires the computational training and institutional access that a PhD provides. The TOPOLOGIX project emerged from a systematic failure. My pre-registered, powered replication of bipartite persistent homology for hERG cardiotoxicity prediction found that topological features do not beat a plain descriptor baseline, with an AUROC of 0.8426 versus 0.8782. This settled a comparison the published literature had never actually run. Applying the same topological constructs to drug-resistance prediction produced AUROC values of 0.425 and 0.485 on the Platinum benchmark, ruling out interface geometry as the driver. TOPOLOGIX replaces structure with sequence: ESM-2 delta-embeddings plus Morgan fingerprints and a Random Forest classifier achieve an AUROC of 0.804 on Platinum and 0.634 on SKEMPI 2.0, while covering all mutations. This is a practical tool for predicting resistance in pathogens where structural data is unavailable, which is the norm in low-resource settings. The ergofluids project tests whether Koopman-operator methods with a Mori-Zwanzig memory kernel can model drug-vehicle transport through dense tumor tissue. The pre-registered pipeline passed synthetic-data gates but failed the first real-data gate against digitized published figures. This negative result was reported directly rather than reframed. The project is methods-validation research, not a venture, and no intellectual property or product claims are made. The PhD I propose would integrate these lines into a single framework: a computational pharmacology platform that predicts both addiction vulnerability and drug-resistance emergence from molecular and circuit-level data, validated against clinical and behavioral datasets from African populations. The M.Sc. Digital Health at HPI provides the technical foundation in health data science, while the PhD would add domain-specific training in computational neuroscience and protein engineering. CURRICULUM VITAE ENIOLA AYODELE OLUTOGUN Contact: Available upon request ORCID: 0009-0001-9272-6735 GitHub: github.com/AmunRaPtah Personal site: zyco.org Nationality: Nigerian Age: 29 EDUCATION B.Pharm, University of Ibadan, Nigeria, 2014-2021 CGPA 5.1/7.0 (2:1 Upper Division), German equivalent 1.9 Licensed by the Pharmacists Council of Nigeria M.Sc. Digital Health, Hasso Plattner Institute / University of Potsdam, Germany Enrolled, Winter Semester 2026/27 RESEARCH EXPERIENCE Independent Computational Researcher, 2024-present - CCT model: tripartite pharmacological framework for reward-memory encoding prevention in addiction. Bayesian MCMC calibration, 14 free parameters, literature-elicited priors from 1,847-record screen. Five pre-registered hypotheses confirmed. Three sole-authored preprints. Co-authored paper under review at Alcohol. - Cardiotoxicity topology study: pre-registered replication of bipartite persistent homology for hERG cardiotoxicity prediction. Topological features did not beat plain descriptor baseline (AUROC 0.8426 vs 0.8782). - Interface-topology-for-resistance study: bipartite persistent homology applied to drug-resistance prediction. AUROC 0.425 and 0.485 on Platinum benchmark. - TOPOLOGIX: ESM-2 protein-language-model delta-embeddings plus Morgan fingerprints and Random Forest classifier. AUROC 0.804 on Platinum benchmark, 0.634 on SKEMPI 2.0. - neurocascade: receptor-to-behavior brain-circuit simulation engine. Coupled pharmacokinetics to receptor-binding to Wilson-Cowan circuit dynamics. 62 of 62 tests passing. - ergofluids: Koopman-operator methods with Mori-Zwanzig memory kernel for drug-vehicle transport modeling. Pre-registered pipeline, negative result reported directly. Research Assistant, CDDDP, University of Ibadan NMDA/insulin docking studies Bioinformatics Researcher, GHRU-GSAR Antimicrobial resistance genomics and surveillance pipeline development EMPLOYMENT National Product Manager, Synthcare, March 2026-present Clinical Pharmacist, Ramset Pharmacy, January-March 2026 ENDORSEMENTS AND COLLABORATIONS Kent Berridge, University of Michigan Samuel Gershman, Harvard University (arXiv endorsement) Nathaniel Daw, Princeton University Marcelo Mattar, New York University SKILLS Programming: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, JavaScript/Node.js Computational tools: TDA (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock Infrastructure: Nextflow/SLURM/HPC, Supabase/Postgres, DuckDB, llama.cpp, Linux VPS, systemd, Caddy TLS, CI/CD Data pipelines: Four independent DuckDB-based ingest-to-analyze corpus/RAG pipelines across life-sciences, tech/AI/security, and social-science domains PUBLICATIONS AND PREPRINTS Three sole-authored preprints on CCT model (OSF/Zenodo) Co-authored paper in Alcohol (Elsevier, under review) CHECKLIST - [ ] Completed application form for Women PhD Scholarships for International Students 2026-2027 - [ ] Motivation letter (this document) - [ ] Research statement (this document) - [ ] Curriculum vitae (this document) - [ ] Academic transcripts from University of Ibadan (B.Pharm) - [ ] Proof of enrollment or admission to M.Sc. Digital Health at HPI/Potsdam - [ ] Two letters of recommendation: one from a computational neuroscience collaborator (e.g., Kent Berridge or Samuel Gershman), one from a pharmacology or digital health faculty member - [ ] Copy of PCN pharmacist license - [ ] Preprints and published papers (CCT model preprints, Alcohol paper if accepted) - [ ] ORCID profile printout - [ ] Personal identification (passport copy) - [ ] Proof of Nigerian nationality EDITOR NOTES - Eligibility risk: The programme is listed as "Women PhD Scholarships for International Students" but the provider is "ScholarshipBob," which appears to be an aggregator site rather than a direct funder. Verify whether the actual funding body is a specific university, foundation, or government programme. If ScholarshipBob is only a listing service, identify the real provider and adjust all references accordingly. - Fact verification: Confirm that the M.Sc. Digital Health at HPI/Potsdam is indeed a master's programme that can serve as a pathway to a PhD. Some German MSc programmes are consecutive and do not automatically lead to a doctoral track. Verify the specific structure. - Personal detail gap: The profile does not include a statement of financial need or specific cost estimates for the MSc programme. The applicant should insert tuition fees, living costs in Potsdam, and any existing funding sources to demonstrate the gap the scholarship would fill. - Recommendation letter strategy: The endorsements listed are from senior researchers who have not co-authored papers with the applicant. Confirm that each is willing to write a letter specifically for this scholarship, and that they can speak to the applicant's research independence and potential for doctoral work. - Publication status: The Alcohol paper is listed as "under review." If it is accepted before the deadline, update the CV and research statement. If it is rejected, note that the preprints remain as evidence of research output.