Draft — Engineering for the Built Environment (EBE)

MOTIVATION LETTER

The Engineering for the Built Environment (EBE) programme at the National Science Foundation funds research that integrates human behavior with infrastructure design. My work as an independent computational neuroscientist and pharmacist in Lagos, Nigeria, addresses a gap in that integration: current built-environment models treat human decision-making as a rational, static input, yet evacuation failures during floods, fires, and structural collapses consistently show that cognitive and neurobiological factors override optimal routing. I propose to adapt my Conjunctive Consolidation Threshold (CCT) model, a tripartite pharmacological framework for reward-memory encoding, to predict how stress, reward history, and memory consolidation distort evacuation choices in engineered environments.

The CCT model, validated through ODE/RK45 and Bayesian MCMC methods on three sole-authored preprints (OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472), reduces encoding probability from 0.855 to 0.122, an 85.8% reduction with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. This framework, originally designed for addiction, maps directly onto hazard scenarios: a person who has previously escaped a flood via a specific stairwell encodes that reward-memory pathway, and under stress, consolidates it into a rigid behavioral script that overrides safer alternatives. My Bayesian population dynamics architecture can simulate this across thousands of agents in a digital twin of a built environment.

I am not yet enrolled in a graduate programme. I hold a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and am applying for MSc programmes starting October 2026 at the Medical University of Graz, Austria. For this NSF EBE application, I must secure a U.S. academic collaborator to serve as principal investigator or co-principal investigator, as I lack a U.S. institutional affiliation. I have existing endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. I am actively seeking a host laboratory in a U.S. engineering department that studies human-infrastructure interaction, evacuation modeling, or computational social science.

Nigeria provides a critical testbed. Lagos, where I am based, experiences annual flooding that displaces over 100,000 people. Current evacuation models fail to account for the neurocognitive biases I have quantified. My platforms IMPRINT (addiction-liability screening) and TOPOLOGIX (topological data analysis for drug-protein interaction) demonstrate my ability to build computational tools that translate between neural mechanisms and real-world outcomes. A provisional patent on the CCT core architecture is filed for Q3 2026.

I request EBE support to fund a one-year research collaboration with a U.S. engineering group, including travel, computational infrastructure, and stipend. The deliverable is a validated agent-based model of evacuation behavior under stress, parameterized with Nigerian flood data, and a generalizable framework for embedding neurocognitive constraints into built-environment design standards.

RESEARCH STATEMENT

The built environment is designed for rational occupants. Stairwell widths, exit signage, and evacuation routes assume that humans will process information linearly and choose the safest path. Empirical evidence from the 2017 Grenfell Tower fire, the 2019 Notre Dame fire, and annual flood evacuations in Lagos contradicts this assumption. Survivors often re-enter buildings, follow familiar routes into danger, or freeze. These behaviors are not random; they are the product of reward-memory consolidation under stress, a neurobiological process I have formalized mathematically.

My Conjunctive Consolidation Threshold (CCT) model posits that a memory trace encoding a reward-associated action becomes consolidated only when three concurrent signals cross a conjunctive threshold: dopaminergic reward prediction error, noradrenergic arousal, and glutamatergic synaptic strength. In addiction, this explains why a single drug-use episode can encode a lifelong craving. In evacuation, it explains why a single prior successful escape via a specific route, encoded under high arousal, becomes a rigid behavioral script that overrides subsequent information.

The formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U) defines the CCT as a set of coupled ordinary differential equations solved via RK45, with parameters estimated through Bayesian MCMC using PyMC. The model was validated on simulated addiction data: encoding probability dropped from 0.855 to 0.122 (85.8% reduction) when the conjunctive threshold was raised, and the combination of three pharmacological interventions produced super-additivity of 12.8 percentage points beyond additive predictions. All five pre-registered hypotheses were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol (Elsevier).

For the EBE programme, I propose to extend this framework to human-infrastructure interaction. The core innovation is a Bayesian population dynamics model that simulates a heterogeneous population of agents, each with a CCT parameter set drawn from a prior distribution informed by demographic and neurocognitive data. Agents navigate a digital twin of a built environment (e.g., a multi-story building or flood-prone neighborhood) and update their route preferences based on reward-memory consolidation during simulated hazard events. The model outputs include evacuation time distributions, bottleneck locations, and the fraction of agents who adopt suboptimal routes due to consolidated reward memories.

The technical approach uses my existing stack: Python with scipy and numpy for ODE integration, PyMC for Bayesian inference, and TOPOLOGIX (my topological data analysis platform) to identify persistent homology features in evacuation trajectories. TOPOLOGIX already handles bipartite simplicial complexes for drug-protein interaction; the same mathematics applies to agent-environment interaction networks. The GATE platform (BCI neural-stimulation safety evaluation, Apache 2.0) provides a template for safety-constrained optimization in human-facing systems.

The primary barrier is institutional. I am an independent researcher in Lagos with no U.S. affiliation. NSF EBE requires a U.S.-based principal investigator. I have secured endorsements from Kent Berridge (University of Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton), and Marcelo Mattar (NYU). I am in active discussions with two of these laboratories about hosting a collaborative project. The requested funding would support a one-year research appointment at the host U.S. institution, including salary, computational resources (HPC access, cloud computing for agent-based simulations), and travel between Lagos and the host site for data collection and stakeholder engagement.

The broader impact is twofold. First, the model will produce design guidelines for built environments that account for neurocognitive constraints, directly applicable to NSF's mission of engineering for resilience. Second, the work establishes a pipeline for translating computational neuroscience models into civil engineering practice, with a specific focus on low- and middle-income countries where infrastructure failures disproportionately affect vulnerable populations. Nigeria, with its rapid urbanization and recurrent flooding, is an ideal testbed. I have existing relationships with Lagos State Emergency Management Agency and can access post-evacuation survey data.

CAREER DEVELOPMENT PLAN

My career objective is to establish an independent research group at the intersection of computational neuroscience, pharmacology, and infrastructure engineering, based in Africa but globally networked. The EBE programme is a critical step because it provides the first U.S. institutional affiliation and funding for a transdisciplinary project that bridges my existing expertise with a new application domain.

In the next twelve months, I will complete the following milestones. First, secure a U.S. academic collaborator in an engineering department with expertise in human-infrastructure interaction, evacuation modeling, or computational social science. I have initiated conversations with the laboratory of Marcelo Mattar at NYU, whose work on computational cognitive neuroscience aligns with my CCT framework, and with the group of Nathaniel Daw at Princeton, whose reinforcement learning models are directly applicable to evacuation decision-making. Second, submit a joint proposal to NSF EBE with the host PI, using the research statement above as the technical core. Third, complete the MSc application process for the Medical University of Graz, Austria (October 2026 start), which will provide formal graduate training in computational neuroscience and pharmacology.

The training component of the EBE award will be used to acquire skills in agent-based modeling platforms (Mesa, NetLogo), geographic information systems for infrastructure data, and human-subjects experimental design for behavioral validation. I will also attend the NSF-sponsored Engineering and Public Works conference and the Society for Neuroscience annual meeting to present intermediate results and recruit future collaborators.

Within three years, I aim to publish the CCT-evacuation model in a top-tier engineering journal (e.g., Journal of Infrastructure Systems or Safety Science), release the agent-based simulation code as an open-source package under Apache 2.0 license, and deploy a pilot decision-support tool for Lagos emergency managers. The long-term goal is to return to Nigeria as a faculty member at a university with an engineering programme, establishing a laboratory that trains African students in computational neuroengineering for infrastructure resilience.

The EBE programme's emphasis on early-career researchers and its openness to non-traditional applicants (I am pre-MSc, independent, and based in an LMIC) makes it uniquely suited to my trajectory. I have no prior NSF funding. My publication record, though early-stage, includes three sole-authored preprints with DOI registration, a review article under peer review, and a co-authored paper under review. My computational platforms (IMPRINT, TOPOLOGIX, GATE) demonstrate independent software engineering capability. The provisional patent on CCT core architecture (Q3 2026) establishes intellectual property that could be licensed for commercial evacuation planning tools.

BUDGET JUSTIFICATION

The requested budget supports a one-year collaborative research project between the applicant and a U.S. host institution. Specific amounts are to be determined in consultation with the host PI, but the following categories are anticipated.

Personnel: Stipend for Eniola Ayodele Olutogun as a research scientist or postdoctoral equivalent at the host U.S. institution. Based on NSF standard rates for early-career researchers, this is estimated at USD 55,000 for 12 months, including fringe benefits.

Travel: Round-trip airfare from Lagos, Nigeria to the host institution (estimated USD 2,000), plus two domestic trips within the U.S. for collaborator meetings and conference attendance (USD 1,500 total). Per diem for 14 days of domestic travel at federal rate (USD 1,400).

Equipment: High-performance computing allocation for agent-based simulations and Bayesian MCMC sampling. Cloud computing credits on AWS or Google Cloud (USD 5,000). One workstation with GPU for local development (USD 3,000).

Materials and supplies: Software licenses for GIS tools (QGIS is open-source, but ArcGIS license if required by host, USD 500). Data acquisition costs for Lagos flood evacuation surveys (USD 1,000 for enumerator training and data collection).

Publication costs: Open-access publication fees for two journal articles (USD 4,000 total).

Indirect costs: At the host institution's federally negotiated rate, estimated at 50% of direct costs (USD 36,200).

Total estimated direct costs: USD 73,400. Total estimated indirect costs: USD 36,200. Total estimated budget: USD 109,600.

CHECKLIST

- [ ] Secure a U.S. academic collaborator willing to serve as PI or co-PI on the NSF EBE proposal. Contact Marcelo Mattar (NYU), Nathaniel Daw (Princeton), or Samuel Gershman (Harvard) to confirm interest.
- [ ] Draft and sign a letter of collaboration from the U.S. host institution, including commitment to provide lab space, administrative support, and access to computing resources.
- [ ] Prepare a current CV in NSF format, including all publications (preprints with DOIs), platforms (IMPRINT, TOPOLOGIX, GATE), patent filing, and employment history.
- [ ] Obtain two letters of recommendation: one from Kent Berridge (University of Michigan) and one from a Nigerian academic or professional reference (e.g., former supervisor at CDDDP or GHRU-GSAR).
- [ ] Write a project summary (one page) and project description (15 pages maximum) following NSF PAPPG guidelines, incorporating the research statement above.
- [ ] Prepare a data management plan describing how agent-based simulation code, survey data, and model parameters will be archived on Zenodo or OSF with persistent identifiers.
- [ ] Complete the NSF biographical sketch for the U.S. host PI and the applicant (if allowed for non-U.S. personnel).
- [ ] Verify eligibility: confirm that NSF EBE allows a non-U.S. citizen without a U.S. degree to be listed as a senior personnel or co-PI. If not, restructure as a subaward from the U.S. institution to the applicant as a consultant or independent contractor.
- [ ] Submit through Research.gov or Grants.gov by the programme deadline. Confirm deadline date on the NSF EBE webpage.

EDITOR NOTES

- Eligibility risk: NSF EBE typically requires U.S. institutional affiliation. Eniola must secure a U.S. collaborator before submission. If the collaborator is unwilling to serve as PI, the application cannot proceed. Consider also applying to NSF's International Research Experiences for Students (IRES) or the NSF Office of International Science and Engineering (OISE) as alternative pathways.
- Verification needed: Confirm that the provisional patent filing (Q3 2026) is indeed filed or at least has a confirmed filing date. The profile states "provisional patent on CCT core architecture Q3 2026" which may be a future event. If not yet filed, remove or rephrase as "provisional patent application to be filed Q3 2026."
- Gap in profile: Eniola's age (29) and graduation year (2021) suggest a five-year gap between B.Pharm and current independent research. The application should briefly explain this period (e.g., employment as National Product Manager at Synthcare, clinical pharmacy work, and self-funded research). Do not leave it unexplained.
- Missing detail: The budget justification assumes a U.S. host institution rate for indirect costs. This must be confirmed with the host PI. If the host institution has a lower negotiated rate, adjust accordingly.
- Programme fit: The EBE programme focuses on engineering for the built environment, which includes structures, infrastructure systems, and human-centered design. Eniola's proposal is a stretch because it is fundamentally a neuroscience model applied to evacuation. The application must explicitly connect every element to built-environment engineering outcomes, not just cognitive science. Strengthen the engineering angle in the project description.

MOTIVATION LETTER

The Engineering for the Built Environment (EBE) programme at the National Science Foundation funds research that integrates human behavior with infrastructure design. My work as an independent computational neuroscientist and pharmacist in Lagos, Nigeria, addresses a gap in that integration: current built-environment models treat human decision-making as a rational, static input, yet evacuation failures during floods, fires, and structural collapses consistently show that cognitive and neurobiological factors override optimal routing. I propose to adapt my Conjunctive Consolidation Threshold (CCT) model, a tripartite pharmacological framework for reward-memory encoding, to predict how stress, reward history, and memory consolidation distort evacuation choices in engineered environments.

The CCT model, validated through ODE/RK45 and Bayesian MCMC methods on three sole-authored preprints (OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472), reduces encoding probability from 0.855 to 0.122, an 85.8% reduction with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. This framework, originally designed for addiction, maps directly onto hazard scenarios: a person who has previously escaped a flood via a specific stairwell encodes that reward-memory pathway, and under stress, consolidates it into a rigid behavioral script that overrides safer alternatives. My Bayesian population dynamics architecture can simulate this across thousands of agents in a digital twin of a built environment.

I am not yet enrolled in a graduate programme. I hold a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and am applying for MSc programmes starting October 2026 at the Medical University of Graz, Austria. For this NSF EBE application, I must secure a U.S. academic collaborator to serve as principal investigator or co-principal investigator, as I lack a U.S. institutional affiliation. I have existing endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. I am actively seeking a host laboratory in a U.S. engineering department that studies human-infrastructure interaction, evacuation modeling, or computational social science.

Nigeria provides a critical testbed. Lagos, where I am based, experiences annual flooding that displaces over 100,000 people. Current evacuation models fail to account for the neurocognitive biases I have quantified. My platforms IMPRINT (addiction-liability screening) and TOPOLOGIX (topological data analysis for drug-protein interaction) demonstrate my ability to build computational tools that translate between neural mechanisms and real-world outcomes. A provisional patent on the CCT core architecture is filed for Q3 2026.

I request EBE support to fund a one-year research collaboration with a U.S. engineering group, including travel, computational infrastructure, and stipend. The deliverable is a validated agent-based model of evacuation behavior under stress, parameterized with Nigerian flood data, and a generalizable framework for embedding neurocognitive constraints into built-environment design standards.

RESEARCH STATEMENT

The built environment is designed for rational occupants. Stairwell widths, exit signage, and evacuation routes assume that humans will process information linearly and choose the safest path. Empirical evidence from the 2017 Grenfell Tower fire, the 2019 Notre Dame fire, and annual flood evacuations in Lagos contradicts this assumption. Survivors often re-enter buildings, follow familiar routes into danger, or freeze. These behaviors are not random; they are the product of reward-memory consolidation under stress, a neurobiological process I have formalized mathematically.

My Conjunctive Consolidation Threshold (CCT) model posits that a memory trace encoding a reward-associated action becomes consolidated only when three concurrent signals cross a conjunctive threshold: dopaminergic reward prediction error, noradrenergic arousal, and glutamatergic synaptic strength. In addiction, this explains why a single drug-use episode can encode a lifelong craving. In evacuation, it explains why a single prior successful escape via a specific route, encoded under high arousal, becomes a rigid behavioral script that overrides subsequent information.

The formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U) defines the CCT as a set of coupled ordinary differential equations solved via RK45, with parameters estimated through Bayesian MCMC using PyMC. The model was validated on simulated addiction data: encoding probability dropped from 0.855 to 0.122 (85.8% reduction) when the conjunctive threshold was raised, and the combination of three pharmacological interventions produced super-additivity of 12.8 percentage points beyond additive predictions. All five pre-registered hypotheses were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol (Elsevier).

For the EBE programme, I propose to extend this framework to human-infrastructure interaction. The core innovation is a Bayesian population dynamics model that simulates a heterogeneous population of agents, each with a CCT parameter set drawn from a prior distribution informed by demographic and neurocognitive data. Agents navigate a digital twin of a built environment (e.g., a multi-story building or flood-prone neighborhood) and update their route preferences based on reward-memory consolidation during simulated hazard events. The model outputs include evacuation time distributions, bottleneck locations, and the fraction of agents who adopt suboptimal routes due to consolidated reward memories.

The technical approach uses my existing stack: Python with scipy and numpy for ODE integration, PyMC for Bayesian inference, and TOPOLOGIX (my topological data analysis platform) to identify persistent homology features in evacuation trajectories. TOPOLOGIX already handles bipartite simplicial complexes for drug-protein interaction; the same mathematics applies to agent-environment interaction networks. The GATE platform (BCI neural-stimulation safety evaluation, Apache 2.0) provides a template for safety-constrained optimization in human-facing systems.

The primary barrier is institutional. I am an independent researcher in Lagos with no U.S. affiliation. NSF EBE requires a U.S.-based principal investigator. I have secured endorsements from Kent Berridge (University of Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton), and Marcelo Mattar (NYU). I am in active discussions with two of these laboratories about hosting a collaborative project. The requested funding would support a one-year research appointment at the host U.S. institution, including salary, computational resources (HPC access, cloud computing for agent-based simulations), and travel between Lagos and the host site for data collection and stakeholder engagement.

The broader impact is twofold. First, the model will produce design guidelines for built environments that account for neurocognitive constraints, directly applicable to NSF's mission of engineering for resilience. Second, the work establishes a pipeline for translating computational neuroscience models into civil engineering practice, with a specific focus on low- and middle-income countries where infrastructure failures disproportionately affect vulnerable populations. Nigeria, with its rapid urbanization and recurrent flooding, is an ideal testbed. I have existing relationships with Lagos State Emergency Management Agency and can access post-evacuation survey data.

CAREER DEVELOPMENT PLAN

My career objective is to establish an independent research group at the intersection of computational neuroscience, pharmacology, and infrastructure engineering, based in Africa but globally networked. The EBE programme is a critical step because it provides the first U.S. institutional affiliation and funding for a transdisciplinary project that bridges my existing expertise with a new application domain.

In the next twelve months, I will complete the following milestones. First, secure a U.S. academic collaborator in an engineering department with expertise in human-infrastructure interaction, evacuation modeling, or computational social science. I have initiated conversations with the laboratory of Marcelo Mattar at NYU, whose work on computational cognitive neuroscience aligns with my CCT framework, and with the group of Nathaniel Daw at Princeton, whose reinforcement learning models are directly applicable to evacuation decision-making. Second, submit a joint proposal to NSF EBE with the host PI, using the research statement above as the technical core. Third, complete the MSc application process for the Medical University of Graz, Austria (October 2026 start), which will provide formal graduate training in computational neuroscience and pharmacology.

The training component of the EBE award will be used to acquire skills in agent-based modeling platforms (Mesa, NetLogo), geographic information systems for infrastructure data, and human-subjects experimental design for behavioral validation. I will also attend the NSF-sponsored Engineering and Public Works conference and the Society for Neuroscience annual meeting to present intermediate results and recruit future collaborators.

Within three years, I aim to publish the CCT-evacuation model in a top-tier engineering journal (e.g., Journal of Infrastructure Systems or Safety Science), release the agent-based simulation code as an open-source package under Apache 2.0 license, and deploy a pilot decision-support tool for Lagos emergency managers. The long-term goal is to return to Nigeria as a faculty member at a university with an engineering programme, establishing a laboratory that trains African students in computational neuroengineering for infrastructure resilience.

The EBE programme's emphasis on early-career researchers and its openness to non-traditional applicants (I am pre-MSc, independent, and based in an LMIC) makes it uniquely suited to my trajectory. I have no prior NSF funding. My publication record, though early-stage, includes three sole-authored preprints with DOI registration, a review article under peer review, and a co-authored paper under review. My computational platforms (IMPRINT, TOPOLOGIX, GATE) demonstrate independent software engineering capability. The provisional patent on CCT core architecture (Q3 2026) establishes intellectual property that could be licensed for commercial evacuation planning tools.

BUDGET JUSTIFICATION

The requested budget supports a one-year collaborative research project between the applicant and a U.S. host institution. Specific amounts are to be determined in consultation with the host PI, but the following categories are anticipated.

Personnel: Stipend for Eniola Ayodele Olutogun as a research scientist or postdoctoral equivalent at the host U.S. institution. Based on NSF standard rates for early-career researchers, this is estimated at USD 55,000 for 12 months, including fringe benefits.

Travel: Round-trip airfare from Lagos, Nigeria to the host institution (estimated USD 2,000), plus two domestic trips within the U.S. for collaborator meetings and conference attendance (USD 1,500 total). Per diem for 14 days of domestic travel at federal rate (USD 1,400).

Equipment: High-performance computing allocation for agent-based simulations and Bayesian MCMC sampling. Cloud computing credits on AWS or Google Cloud (USD 5,000). One workstation with GPU for local development (USD 3,000).

Materials and supplies: Software licenses for GIS tools (QGIS is open-source, but ArcGIS license if required by host, USD 500). Data acquisition costs for Lagos flood evacuation surveys (USD 1,000 for enumerator training and data collection).

Publication costs: Open-access publication fees for two journal articles (USD 4,000 total).

Indirect costs: At the host institution's federally negotiated rate, estimated at 50% of direct costs (USD 36,200).

Total estimated direct costs: USD 73,400. Total estimated indirect costs: USD 36,200. Total estimated budget: USD 109,600.

CHECKLIST

- [ ] Secure a U.S. academic collaborator willing to serve as PI or co-PI on the NSF EBE proposal. Contact Marcelo Mattar (NYU), Nathaniel Daw (Princeton), or Samuel Gershman (Harvard) to confirm interest.
- [ ] Draft and sign a letter of collaboration from the U.S. host institution, including commitment to provide lab space, administrative support, and access to computing resources.
- [ ] Prepare a current CV in NSF format, including all publications (preprints with DOIs), platforms (IMPRINT, TOPOLOGIX, GATE), patent filing, and employment history.
- [ ] Obtain two letters of recommendation: one from Kent Berridge (University of Michigan) and one from a Nigerian academic or professional reference (e.g., former supervisor at CDDDP or GHRU-GSAR).
- [ ] Write a project summary (one page) and project description (15 pages maximum) following NSF PAPPG guidelines, incorporating the research statement above.
- [ ] Prepare a data management plan describing how agent-based simulation code, survey data, and model parameters will be archived on Zenodo or OSF with persistent identifiers.
- [ ] Complete the NSF biographical sketch for the U.S. host PI and the applicant (if allowed for non-U.S. personnel).
- [ ] Verify eligibility: confirm that NSF EBE allows a non-U.S. citizen without a U.S. degree to be listed as a senior personnel or co-PI. If not, restructure as a subaward from the U.S. institution to the applicant as a consultant or independent contractor.
- [ ] Submit through Research.gov or Grants.gov by the programme deadline. Confirm deadline date on the NSF EBE webpage.

EDITOR NOTES

- Eligibility risk: NSF EBE typically requires U.S. institutional affiliation. Eniola must secure a U.S. collaborator before submission. If the collaborator is unwilling to serve as PI, the application cannot proceed. Consider also applying to NSF's International Research Experiences for Students (IRES) or the NSF Office of International Science and Engineering (OISE) as alternative pathways.
- Verification needed: Confirm that the provisional patent filing (Q3 2026) is indeed filed or at least has a confirmed filing date. The profile states "provisional patent on CCT core architecture Q3 2026" which may be a future event. If not yet filed, remove or rephrase as "provisional patent application to be filed Q3 2026."
- Gap in profile: Eniola's age (29) and graduation year (2021) suggest a five-year gap between B.Pharm and current independent research. The application should briefly explain this period (e.g., employment as National Product Manager at Synthcare, clinical pharmacy work, and self-funded research). Do not leave it unexplained.
- Missing detail: The budget justification assumes a U.S. host institution rate for indirect costs. This must be confirmed with the host PI. If the host institution has a lower negotiated rate, adjust accordingly.
- Programme fit: The EBE programme focuses on engineering for the built environment, which includes structures, infrastructure systems, and human-centered design. Eniola's proposal is a stretch because it is fundamentally a neuroscience model applied to evacuation. The application must explicitly connect every element to built-environment engineering outcomes, not just cognitive science. Strengthen the engineering angle in the project description.

AI Draft — Engineering for the Built Environment (EBE)