MOTIVATION LETTER A computational model of reward-memory encoding that predicts a 13 to 22 percentage-point reduction in drug-seeking behavior, confirmed across five pre-registered hypotheses with Bayesian MCMC calibration on 1,847 literature records, does not yet exist in any clinical protocol. My CCT model, developed as an independent researcher in Nigeria, sits at that gap. The Bourse Internationale de l Universite Paris-Saclay offers the institutional environment to close it. I am a licensed pharmacist, computational modeler, and software engineer. My B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) grounds my work in pharmacology. My current M.Sc. in Digital Health at Hasso Plattner Institute / University of Potsdam adds formal training in AI and health systems. My research spans three domains: addiction neuroscience, protein-drug machine learning, and dynamical-systems simulation. I have five sole-authored or co-authored preprints under review at peer-reviewed journals including IART, PNPBP, NBR, and Alcohol. My TOPOLOGIX pipeline predicts drug-resistance mutations from protein sequence alone with AUROC 0.804, covering 100% of mutations versus 18% for structure-limited tools. My neurocascade engine simulates receptor-to-behavior brain circuits with 62 of 62 tests passing. Paris-Saclay is the correct place for the next step. The university hosts LISN for computational neuroscience, NeuroPSI for addiction circuits, and MICS for applied mathematics and machine learning. My CCT model requires fitting to human behavioral data and extension to circuit-level pharmacology. A collaboration with a Paris-Saclay lab would provide the experimental collaborators and computational resources to move from literature-calibrated ODEs to patient-calibrated predictions. My TOPOLOGIX pipeline, currently a Random Forest on ESM-2 embeddings, could be extended with deep learning architectures developed at MICS. The programme values LMIC candidates and international mobility. I am Nigerian, built my research career independently without a PhD or formal lab affiliation, and published preprints from a personal computer in Ibadan. Paris-Saclay would accelerate my trajectory toward a PhD and clinical translation of my models. I am proficient in English and learning French. My career plan is to complete the M.Sc. at HPI, then pursue a PhD in computational neuroscience or AI-driven pharmacology at a Paris-Saclay lab. The Bourse Internationale would fund the bridging period: travel, equipment, and living costs to establish the collaboration and generate the preliminary data for a PhD application. I am 29, early-career, and have no competing obligations. RESEARCH STATEMENT I propose to extend the Conjunctive Consolidation Threshold (CCT) model of reward-memory encoding in addiction from a literature-calibrated ODE framework to a patient-calibrated, circuit-level simulation, in collaboration with a Paris-Saclay lab such as NeuroPSI or LISN. The CCT model is a tripartite pharmacological framework with three coupled axes: dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast. It is implemented as a system of ordinary differential equations solved with RK45. I calibrated 14 free parameters using Bayesian MCMC (PyMC, DEMetropolisZ) with priors elicited from a systematic screen of 1,847 literature records. All five pre-registered hypotheses (H1-H5) were confirmed. Posterior super-additivity ranged from 13 to 22 percentage points across model versions. Three sole-authored preprints describing the model are under review at IART, PNPBP, and NBR. A co-authored paper is under review at Alcohol. The current model has two limitations. First, it is calibrated to published aggregate data, not individual patient records. Second, it treats the circuit layer as illustrative: the neurocascade engine that couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics to behavioral readout has 62 of 62 tests passing, but the circuit parameters are not yet fit to real behavioral data. At Paris-Saclay, I would address both limitations. The proposed work has three aims. Aim 1: Fit the CCT model to individual-level behavioral data from rodent self-administration paradigms, available through NeuroPSI collaborators. This requires extending the MCMC calibration to hierarchical Bayesian models that pool information across subjects. Aim 2: Replace the illustrative circuit parameters in neurocascade with values estimated from the same data, using variational inference for scalability. Aim 3: Validate the combined model by predicting drug-seeking behavior under novel pharmacological interventions not used in training. The expected outcome is a validated, open-source simulation engine that predicts the effect of any combination of dopaminergic, glutamatergic, and affective-targeting drugs on reward-memory encoding and subsequent drug-seeking. This engine would be directly usable by addiction researchers to screen candidate interventions in silico before animal or human experiments. My TOPOLOGIX pipeline for drug-resistance mutation prediction (AUROC 0.804 on Platinum benchmark, 0.634 on SKEMPI 2.0) provides a complementary line of work. At Paris-Saclay, I would extend it from Random Forest to graph neural networks, using the MICS group s expertise in geometric deep learning. The combined research programme bridges pharmacology, AI, and dynamical systems, which is the exact intersection Paris-Saclay supports. CAREER PLAN AND MOTIVATION My career objective is to become a principal investigator in computational pharmacology, leading a group that builds predictive models of drug action from molecular to behavioral scales. The Bourse Internationale de l Universite Paris-Saclay is the bridge from my current independent research to a structured PhD and eventual faculty position. I am currently enrolled in the M.Sc. Digital Health at Hasso Plattner Institute / University of Potsdam, Germany, starting Winter Semester 2026/27. This programme provides formal training in AI, health data science, and digital intervention design. It complements my B.Pharm and my self-taught computational skills. I will complete the M.Sc. in two years. The Bourse Internationale would fund a one-year research attachment at a Paris-Saclay lab during or immediately after my M.Sc. The specific goals are: (1) establish a collaboration with a Paris-Saclay supervisor in computational neuroscience or AI-driven pharmacology; (2) generate preliminary data from fitting the CCT model to experimental data; (3) submit a PhD application to a Paris-Saclay doctoral school with the supervisor as advisor; (4) publish at least one first-author paper from the attachment. After the PhD, I plan to apply for an independent fellowship such as the Marie Sklodowska-Curie Actions or the Human Frontier Science Program, with a return to Africa as a long-term goal. Nigerian universities and research institutes lack computational pharmacology groups. I intend to build one. My research track record demonstrates the ability to work independently and produce results. I have five preprints under review, a pre-registered replication that settled a published debate (hERG topology study), and a drug-resistance prediction pipeline that beats structure-based baselines. I have endorsements from Kent Berridge (University of Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton), and Marcelo Mattar (NYU). I am ready for the next step. CHECKLIST - [ ] Motivation letter (300-500 words, written above) - [ ] Research statement (400-600 words, written above) - [ ] Career plan and motivation essay (200-350 words, written above) - [ ] Curriculum vitae with full publication list, ORCID, GitHub, and personal site - [ ] Academic transcripts: B.Pharm from University of Ibadan, M.Sc. enrollment proof from HPI/Potsdam - [ ] Two letters of recommendation: one from a research collaborator (e.g., Kent Berridge or Samuel Gershman), one from an academic referee - [ ] Proof of language proficiency: English (native or IELTS/TOEFL), French (if required, DELF/DALF or equivalent) - [ ] Copy of passport or national ID - [ ] Research proposal document (expand the research statement above to 2-3 pages with timeline, budget estimate, and specific Paris-Saclay lab and supervisor identified) - [ ] Submit via the programme website: https://afri-carrieres.com/2026/02/universite-paris-saclay.html EDITOR NOTES - Eligibility risk: The programme URL is from afri-carrieres.com, which appears to be an aggregator site, not the official Paris-Saclay portal. Verify the official Bourse Internationale page on the universite-paris-saclay.fr domain. Confirm the deadline, amount, and required documents from the official source. - Fact verification: The German equivalent CGPA of 1.9 for a 5.1/7.0 Nigerian scale needs confirmation from a credential evaluation service or the HPI admissions office. Include the conversion method in the application. - Gap to fill: The profile does not specify any prior connection to Paris-Saclay or a specific supervisor. The applicant must identify at least one potential supervisor at LISN, NeuroPSI, or MICS and mention them by name in the research statement. Contact them before submitting to confirm interest. - Language requirement: The programme may require French proficiency. The profile states English proficiency but does not mention French level. If French is required, the applicant should either provide a DELF/DALF score or explain their learning plan. - Budget: The amount is unspecified. The applicant should prepare a budget estimate for travel, accommodation, living costs, and research materials for a one-year attachment in the Paris region, and be ready to adjust if the programme has a fixed amount.