Draft — FRIAS Fellowship Programmes 2027/28

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model proposes a tripartite pharmacological mechanism for preventing reward-memory encoding in addiction. Three coupled axes, dopaminergic reward-prediction error, NMDAR-dependent long-term potentiation, and affective contrast, form an ordinary differential equation system solved with RK45. Bayesian MCMC calibration with PyMC and DEMetropolisZ sampled 14 free parameters against literature-elicited priors drawn from a systematic screen of 1,847 records. All five pre-registered hypotheses, H1 through H5, were confirmed. Posterior super-additivity ranged from 13 to 22 percentage points across model versions. Three sole-authored preprints are deposited on OSF and Zenodo. A co-authored paper is under review at Alcohol, Elsevier.

This work sits at the intersection of computational neuroscience, pharmacology, and dynamical systems. The FRIAS Early Career Fellowship Programme 2027/28 offers the dedicated research time and interdisciplinary environment needed to take the next step: validating the CCT model against real behavioral data and extending the neurocascade simulation engine to make clinical predictions. Freiburg's neuroscience community and computational resources are a natural fit for this programme of work.

My research trajectory has been independent from the start. I hold a B.Pharm from the University of Ibadan, Nigeria, with a CGPA of 5.1 out of 7.0, a German equivalent of 1.9, and I am a PCN-licensed pharmacist. I am currently enrolled in the M.Sc. Digital Health programme at the Hasso Plattner Institute and the University of Potsdam, Germany, starting winter semester 2026/27. My collaborators include Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. These relationships demonstrate that my independent work has earned recognition from established laboratories.

Beyond the CCT model, my research portfolio spans multiple domains. A pre-registered replication study on hERG cardiotoxicity tested whether bipartite persistent homology could predict toxicity from protein-ligand interface geometry. The topological features did not beat a plain descriptor baseline, AUROC 0.8426 versus 0.8782, settling a comparison the published literature had never actually run. A follow-up study applied the same topological constructs to drug-resistance prediction and found they carried almost no signal, with AUROCs of 0.425 and 0.485 on the Platinum benchmark. The current TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings combined with Morgan and ECFP drug fingerprints and a Random Forest classifier, achieving an AUROC of 0.804 plus or minus 0.025 on the Platinum benchmark covering 553 mutations, and 0.634 on SKEMPI 2.0. This approach beats structure-based baselines such as mCSM-lig at approximately 0.70 while covering 100 percent of mutations versus roughly 18 percent for structure-limited tools.

The neurocascade simulation engine couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics to behavioral-readout ODE layers. Three literature-calibrated receptor and circuit systems, mu-opioid, D2 dopamine, and GABA-A, have been Bayesian-calibrated with PyMC. All 62 tests pass. Circuit-layer parameters are explicitly labeled illustrative pending real behavioral-data fits. A FRIAS fellowship would allow me to fit those parameters to actual data and move the engine from a simulation tool to a clinical prediction platform.

As a Nigerian researcher working independently across computational pharmacology, neuroscience, and machine learning, I bring a perspective that is underrepresented in European research institutes. The FRIAS fellowship would enable me to contribute to the programme's interdisciplinary mission while advancing a model system that could inform addiction treatment strategies relevant to both global and African contexts.

RESEARCH STATEMENT

My research programme addresses a central question in computational pharmacology: can we build predictive, mechanism-grounded models of how drugs affect neural circuits and behavior, and can those models generalize across pharmacological targets and clinical conditions? The work proceeds along three interconnected lines.

The first line is the Conjunctive Consolidation Threshold model. This framework proposes that reward-memory encoding in addiction requires the simultaneous activation of three systems: dopaminergic reward-prediction error signaling, NMDAR-dependent long-term potentiation, and affective contrast. The model is implemented as a system of coupled ordinary differential equations solved with RK45. Bayesian MCMC calibration using PyMC's DEMetropolisZ sampler estimated 14 free parameters against priors derived from a systematic literature screen of 1,847 records. All five pre-registered hypotheses were confirmed. Posterior super-additivity ranged from 13 to 22 percentage points across model versions, indicating that the three axes interact non-additively to produce memory consolidation. Three sole-authored preprints are available on OSF and Zenodo. A co-authored paper is under review at Alcohol, Elsevier.

The second line is the neurocascade simulation engine. This tool couples four layers: pharmacokinetics, receptor binding, Wilson-Cowan circuit dynamics, and behavioral readout. Three receptor and circuit systems have been calibrated from literature data: mu-opioid, D2 dopamine, and GABA-A. Bayesian calibration with PyMC produced 62 passing tests. The circuit-layer parameters are currently labeled illustrative because they have not yet been fitted to real behavioral data. The next step is to fit these parameters to existing rodent and human behavioral datasets, which would transform neurocascade from a simulation tool into a predictive clinical platform.

The third line is computational structural pharmacology. The TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings combined with Morgan and ECFP drug fingerprints and a Random Forest classifier to predict drug-resistance mutations from sequence alone. On the Platinum benchmark of 553 mutations, the model achieves an AUROC of 0.804 plus or minus 0.025. On SKEMPI 2.0, the AUROC is 0.634. This approach covers 100 percent of mutations, compared to roughly 18 percent for structure-limited tools, and beats structure-based baselines such as mCSM-lig at approximately 0.70. Two pre-registered studies using bipartite persistent homology for hERG cardiotoxicity and drug-resistance prediction established that topological features of protein-ligand interfaces do not outperform simpler descriptor-based methods for these tasks.

During a FRIAS fellowship, I would pursue three specific aims. First, fit the neurocascade circuit-layer parameters to rodent self-administration data and human fMRI datasets to validate the CCT model predictions. Second, extend TOPOLOGIX to predict resistance mutations for opioid and stimulant targets, creating a tool that could inform personalized addiction pharmacotherapy. Third, develop a combined CCT-neurocascade framework that can simulate the effects of candidate pharmacotherapies on reward-memory consolidation and generate testable predictions for preclinical experiments.

The FRIAS environment is ideal for this work because of its interdisciplinary structure and its location within Freiburg's neuroscience and computational research community. Access to collaborators in systems neuroscience, clinical pharmacology, and machine learning would accelerate all three aims. The fellowship would also allow me to establish a research group focused on computational addiction neuroscience, a field that remains underdeveloped despite the global burden of substance use disorders.

CURRICULUM VITAE

ENIOLA AYODELE OLUTOGUN

ORCID: 0009-0001-9272-6735
GitHub: github.com/AmunRaPtah
Personal site: zyco.org
Nationality: Nigerian
Date of birth: [insert date]

EDUCATION

M.Sc. Digital Health, Hasso Plattner Institute / University of Potsdam, Germany
Winter Semester 2026/27, enrolled

B.Pharm, University of Ibadan, Nigeria, 2014-2021
CGPA 5.1/7.0 (2:1 Upper Division), German equivalent 1.9
PCN-licensed pharmacist

RESEARCH POSITIONS

Independent Researcher, 2024-present

National Product Manager, Synthcare, March 2026-present

Clinical Pharmacist, Ramset Pharmacy, January-March 2026

Research Assistant, CDDDP, NMDA/insulin docking studies

Bioinformatics Researcher, GHRU-GSAR, antimicrobial resistance genomics and surveillance pipeline

PUBLICATIONS AND PREPRINTS

Olutogun, E.A. Conjunctive Consolidation Threshold: A tripartite pharmacological framework for reward-memory encoding prevention in addiction. Three sole-authored preprints. OSF and Zenodo, 2024-2026.

Olutogun, E.A. et al. [Title]. Alcohol, Elsevier. Under review.

Olutogun, E.A. Cardiotoxicity topology study: bipartite persistent homology does not predict hERG cardiotoxicity beyond plain descriptors. Pre-registered replication, 2025.

Olutogun, E.A. Interface-topology-for-resistance study: bipartite persistent homology carries no signal for drug-resistance prediction. Platinum benchmark AUROC 0.425 and 0.485, 2025.

Olutogun, E.A. TOPOLOGIX: ESM-2 delta-embeddings plus Morgan/ECFP fingerprints for drug-resistance mutation prediction. AUROC 0.804 on Platinum benchmark, 0.634 on SKEMPI 2.0, 2026.

Olutogun, E.A. neurocascade: a receptor-to-behavior brain-circuit simulation engine. 62/62 tests passing, 2025.

Olutogun, E.A. ergofluids: Koopman-operator and Dynamic Mode Decomposition with Mori-Zwanzig memory kernel for drug-vehicle transport modeling. Pre-registered, 2025.

COLLABORATORS

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: NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock
Topological data analysis: Ripser, Gudhi
Infrastructure: Nextflow/SLURM/HPC, Supabase/Postgres, DuckDB, llama.cpp, Linux VPS, systemd, Caddy TLS, CI/CD, automated backup/disaster-recovery
Data pipelines: Four independent DuckDB-based ingest-to-analyze corpus/RAG pipelines across life sciences, tech/AI/security, and social science domains

LANGUAGES

English: Native
Yoruba: Native
German: Basic (currently enrolled in language courses)

CHECKLIST

- [ ] Completed FRIAS Early Career Fellowship application form
- [ ] Motivation letter (300-500 words)
- [ ] Research statement (400-600 words)
- [ ] Curriculum vitae with full publication list
- [ ] List of three referees with contact information
- [ ] Copies of three most significant publications or preprints
- [ ] Proof of enrollment in M.Sc. Digital Health at HPI/Potsdam
- [ ] Proof of B.Pharm degree and PCN license
- [ ] Evidence of research stay abroad (Germany enrollment, US collaborations)
- [ ] Letter of recommendation from one collaborator (Berridge, Gershman, Daw, or Mattar)
- [ ] Statement of interdisciplinary potential and fit with FRIAS community
- [ ] Declaration of independent research status

EDITOR NOTES

- Eligibility risk: The FRIAS Early Career Fellowship requires a completed PhD. Eniola holds a B.Pharm and is enrolled in an M.Sc. programme. He does not have a PhD. This is a critical eligibility gap. The application may need to target a different track or programme that accepts pre-PhD researchers, or Eniola must confirm whether the programme makes exceptions for exceptional early-career researchers with substantial publication records. Verify directly with FRIAS before submitting.

- The profile states Eniola is 29 years old. The application should confirm that this falls within any age limits for early-career programmes. Some fellowships have upper age limits of 30 or 35.

- The list of publications includes preprints and papers under review. The application should clarify which items are peer-reviewed and which are not. Some fellowship committees discount preprints.

- The ergofluids project is described as methods-validation research with no IP or product claims. This is fine for a research fellowship, but the application should not frame it as translational or commercial work.

- The Sustainable Governance Senior Fellowship for Researchers from Africa requires a Master's or PhD earned in Africa and a position at an African institution. Eniola's B.Pharm was earned in Nigeria, but he is currently enrolled in a German M.Sc. programme and employed in Nigeria. Verify whether this track is a better fit despite the senior requirement, or whether the early-career track is the only viable option.

- The profile lists collaborators but does not specify whether any have agreed to write letters of recommendation. Confirm with Berridge, Gershman, Daw, and Mattar before listing them as referees.

- The application requires evidence of research or study abroad. Eniola's enrollment in a German M.Sc. programme and collaborations with US-based researchers satisfy this criterion. Provide documentation of enrollment and any correspondence with US collaborators.

- The research statement should include a timeline for the fellowship period. Specify what will be accomplished in year one versus year two.

- The curriculum vitae should include the exact dates of all preprints and publications. The profile provides years but not months. Add months for precision.

- Verify that the FRIAS programme accepts applications from independent researchers without a university affiliation. Eniola is listed as an independent researcher. Some programmes require a host institution. If required, identify a potential host at the University of Freiburg and secure a letter of support.

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model proposes a tripartite pharmacological mechanism for preventing reward-memory encoding in addiction. Three coupled axes, dopaminergic reward-prediction error, NMDAR-dependent long-term potentiation, and affective contrast, form an ordinary differential equation system solved with RK45. Bayesian MCMC calibration with PyMC and DEMetropolisZ sampled 14 free parameters against literature-elicited priors drawn from a systematic screen of 1,847 records. All five pre-registered hypotheses, H1 through H5, were confirmed. Posterior super-additivity ranged from 13 to 22 percentage points across model versions. Three sole-authored preprints are deposited on OSF and Zenodo. A co-authored paper is under review at Alcohol, Elsevier.

This work sits at the intersection of computational neuroscience, pharmacology, and dynamical systems. The FRIAS Early Career Fellowship Programme 2027/28 offers the dedicated research time and interdisciplinary environment needed to take the next step: validating the CCT model against real behavioral data and extending the neurocascade simulation engine to make clinical predictions. Freiburg's neuroscience community and computational resources are a natural fit for this programme of work.

My research trajectory has been independent from the start. I hold a B.Pharm from the University of Ibadan, Nigeria, with a CGPA of 5.1 out of 7.0, a German equivalent of 1.9, and I am a PCN-licensed pharmacist. I am currently enrolled in the M.Sc. Digital Health programme at the Hasso Plattner Institute and the University of Potsdam, Germany, starting winter semester 2026/27. My collaborators include Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. These relationships demonstrate that my independent work has earned recognition from established laboratories.

Beyond the CCT model, my research portfolio spans multiple domains. A pre-registered replication study on hERG cardiotoxicity tested whether bipartite persistent homology could predict toxicity from protein-ligand interface geometry. The topological features did not beat a plain descriptor baseline, AUROC 0.8426 versus 0.8782, settling a comparison the published literature had never actually run. A follow-up study applied the same topological constructs to drug-resistance prediction and found they carried almost no signal, with AUROCs of 0.425 and 0.485 on the Platinum benchmark. The current TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings combined with Morgan and ECFP drug fingerprints and a Random Forest classifier, achieving an AUROC of 0.804 plus or minus 0.025 on the Platinum benchmark covering 553 mutations, and 0.634 on SKEMPI 2.0. This approach beats structure-based baselines such as mCSM-lig at approximately 0.70 while covering 100 percent of mutations versus roughly 18 percent for structure-limited tools.

The neurocascade simulation engine couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics to behavioral-readout ODE layers. Three literature-calibrated receptor and circuit systems, mu-opioid, D2 dopamine, and GABA-A, have been Bayesian-calibrated with PyMC. All 62 tests pass. Circuit-layer parameters are explicitly labeled illustrative pending real behavioral-data fits. A FRIAS fellowship would allow me to fit those parameters to actual data and move the engine from a simulation tool to a clinical prediction platform.

As a Nigerian researcher working independently across computational pharmacology, neuroscience, and machine learning, I bring a perspective that is underrepresented in European research institutes. The FRIAS fellowship would enable me to contribute to the programme's interdisciplinary mission while advancing a model system that could inform addiction treatment strategies relevant to both global and African contexts.

RESEARCH STATEMENT

My research programme addresses a central question in computational pharmacology: can we build predictive, mechanism-grounded models of how drugs affect neural circuits and behavior, and can those models generalize across pharmacological targets and clinical conditions? The work proceeds along three interconnected lines.

The first line is the Conjunctive Consolidation Threshold model. This framework proposes that reward-memory encoding in addiction requires the simultaneous activation of three systems: dopaminergic reward-prediction error signaling, NMDAR-dependent long-term potentiation, and affective contrast. The model is implemented as a system of coupled ordinary differential equations solved with RK45. Bayesian MCMC calibration using PyMC's DEMetropolisZ sampler estimated 14 free parameters against priors derived from a systematic literature screen of 1,847 records. All five pre-registered hypotheses were confirmed. Posterior super-additivity ranged from 13 to 22 percentage points across model versions, indicating that the three axes interact non-additively to produce memory consolidation. Three sole-authored preprints are available on OSF and Zenodo. A co-authored paper is under review at Alcohol, Elsevier.

The second line is the neurocascade simulation engine. This tool couples four layers: pharmacokinetics, receptor binding, Wilson-Cowan circuit dynamics, and behavioral readout. Three receptor and circuit systems have been calibrated from literature data: mu-opioid, D2 dopamine, and GABA-A. Bayesian calibration with PyMC produced 62 passing tests. The circuit-layer parameters are currently labeled illustrative because they have not yet been fitted to real behavioral data. The next step is to fit these parameters to existing rodent and human behavioral datasets, which would transform neurocascade from a simulation tool into a predictive clinical platform.

The third line is computational structural pharmacology. The TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings combined with Morgan and ECFP drug fingerprints and a Random Forest classifier to predict drug-resistance mutations from sequence alone. On the Platinum benchmark of 553 mutations, the model achieves an AUROC of 0.804 plus or minus 0.025. On SKEMPI 2.0, the AUROC is 0.634. This approach covers 100 percent of mutations, compared to roughly 18 percent for structure-limited tools, and beats structure-based baselines such as mCSM-lig at approximately 0.70. Two pre-registered studies using bipartite persistent homology for hERG cardiotoxicity and drug-resistance prediction established that topological features of protein-ligand interfaces do not outperform simpler descriptor-based methods for these tasks.

During a FRIAS fellowship, I would pursue three specific aims. First, fit the neurocascade circuit-layer parameters to rodent self-administration data and human fMRI datasets to validate the CCT model predictions. Second, extend TOPOLOGIX to predict resistance mutations for opioid and stimulant targets, creating a tool that could inform personalized addiction pharmacotherapy. Third, develop a combined CCT-neurocascade framework that can simulate the effects of candidate pharmacotherapies on reward-memory consolidation and generate testable predictions for preclinical experiments.

The FRIAS environment is ideal for this work because of its interdisciplinary structure and its location within Freiburg's neuroscience and computational research community. Access to collaborators in systems neuroscience, clinical pharmacology, and machine learning would accelerate all three aims. The fellowship would also allow me to establish a research group focused on computational addiction neuroscience, a field that remains underdeveloped despite the global burden of substance use disorders.

CURRICULUM VITAE

ENIOLA AYODELE OLUTOGUN

ORCID: 0009-0001-9272-6735
GitHub: github.com/AmunRaPtah
Personal site: zyco.org
Nationality: Nigerian
Date of birth: [insert date]

EDUCATION

M.Sc. Digital Health, Hasso Plattner Institute / University of Potsdam, Germany
Winter Semester 2026/27, enrolled

B.Pharm, University of Ibadan, Nigeria, 2014-2021
CGPA 5.1/7.0 (2:1 Upper Division), German equivalent 1.9
PCN-licensed pharmacist

RESEARCH POSITIONS

Independent Researcher, 2024-present

National Product Manager, Synthcare, March 2026-present

Clinical Pharmacist, Ramset Pharmacy, January-March 2026

Research Assistant, CDDDP, NMDA/insulin docking studies

Bioinformatics Researcher, GHRU-GSAR, antimicrobial resistance genomics and surveillance pipeline

PUBLICATIONS AND PREPRINTS

Olutogun, E.A. Conjunctive Consolidation Threshold: A tripartite pharmacological framework for reward-memory encoding prevention in addiction. Three sole-authored preprints. OSF and Zenodo, 2024-2026.

Olutogun, E.A. et al. [Title]. Alcohol, Elsevier. Under review.

Olutogun, E.A. Cardiotoxicity topology study: bipartite persistent homology does not predict hERG cardiotoxicity beyond plain descriptors. Pre-registered replication, 2025.

Olutogun, E.A. Interface-topology-for-resistance study: bipartite persistent homology carries no signal for drug-resistance prediction. Platinum benchmark AUROC 0.425 and 0.485, 2025.

Olutogun, E.A. TOPOLOGIX: ESM-2 delta-embeddings plus Morgan/ECFP fingerprints for drug-resistance mutation prediction. AUROC 0.804 on Platinum benchmark, 0.634 on SKEMPI 2.0, 2026.

Olutogun, E.A. neurocascade: a receptor-to-behavior brain-circuit simulation engine. 62/62 tests passing, 2025.

Olutogun, E.A. ergofluids: Koopman-operator and Dynamic Mode Decomposition with Mori-Zwanzig memory kernel for drug-vehicle transport modeling. Pre-registered, 2025.

COLLABORATORS

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: NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock
Topological data analysis: Ripser, Gudhi
Infrastructure: Nextflow/SLURM/HPC, Supabase/Postgres, DuckDB, llama.cpp, Linux VPS, systemd, Caddy TLS, CI/CD, automated backup/disaster-recovery
Data pipelines: Four independent DuckDB-based ingest-to-analyze corpus/RAG pipelines across life sciences, tech/AI/security, and social science domains

LANGUAGES

English: Native
Yoruba: Native
German: Basic (currently enrolled in language courses)

CHECKLIST

- [ ] Completed FRIAS Early Career Fellowship application form
- [ ] Motivation letter (300-500 words)
- [ ] Research statement (400-600 words)
- [ ] Curriculum vitae with full publication list
- [ ] List of three referees with contact information
- [ ] Copies of three most significant publications or preprints
- [ ] Proof of enrollment in M.Sc. Digital Health at HPI/Potsdam
- [ ] Proof of B.Pharm degree and PCN license
- [ ] Evidence of research stay abroad (Germany enrollment, US collaborations)
- [ ] Letter of recommendation from one collaborator (Berridge, Gershman, Daw, or Mattar)
- [ ] Statement of interdisciplinary potential and fit with FRIAS community
- [ ] Declaration of independent research status

EDITOR NOTES

- Eligibility risk: The FRIAS Early Career Fellowship requires a completed PhD. Eniola holds a B.Pharm and is enrolled in an M.Sc. programme. He does not have a PhD. This is a critical eligibility gap. The application may need to target a different track or programme that accepts pre-PhD researchers, or Eniola must confirm whether the programme makes exceptions for exceptional early-career researchers with substantial publication records. Verify directly with FRIAS before submitting.

- The profile states Eniola is 29 years old. The application should confirm that this falls within any age limits for early-career programmes. Some fellowships have upper age limits of 30 or 35.

- The list of publications includes preprints and papers under review. The application should clarify which items are peer-reviewed and which are not. Some fellowship committees discount preprints.

- The ergofluids project is described as methods-validation research with no IP or product claims. This is fine for a research fellowship, but the application should not frame it as translational or commercial work.

- The Sustainable Governance Senior Fellowship for Researchers from Africa requires a Master's or PhD earned in Africa and a position at an African institution. Eniola's B.Pharm was earned in Nigeria, but he is currently enrolled in a German M.Sc. programme and employed in Nigeria. Verify whether this track is a better fit despite the senior requirement, or whether the early-career track is the only viable option.

- The profile lists collaborators but does not specify whether any have agreed to write letters of recommendation. Confirm with Berridge, Gershman, Daw, and Mattar before listing them as referees.

- The application requires evidence of research or study abroad. Eniola's enrollment in a German M.Sc. programme and collaborations with US-based researchers satisfy this criterion. Provide documentation of enrollment and any correspondence with US collaborators.

- The research statement should include a timeline for the fellowship period. Specify what will be accomplished in year one versus year two.

- The curriculum vitae should include the exact dates of all preprints and publications. The profile provides years but not months. Add months for precision.

- Verify that the FRIAS programme accepts applications from independent researchers without a university affiliation. Eniola is listed as an independent researcher. Some programmes require a host institution. If required, identify a potential host at the University of Freiburg and secure a letter of support.

AI Draft — FRIAS Fellowship Programmes 2027/28