Programme Thesis
The NSF Energy, Water, and Resource Engineering (EWRE) program funds fundamental engineering research that advances the management of energy, water, minerals, and materials, with a strong emphasis on closing resource loops, safeguarding health, and using AI modeling to improve detection and remediation of pathogens and toxins. It exists to support U.S.-based research that creates domestic energy sources, improves water management, and enables a circular economy through chemical, biological, and geophysical processes.
Selection Criteria
- Intellectual Merit: Potential to advance fundamental engineering knowledge in energy, water, or resource management.
- Broader Impacts: Benefits to society, including economic competitiveness, health safety, and environmental sustainability.
- Alignment with EWRE focus areas: AI modeling, circular economy, resource recovery, detection of pathogens/toxins, and advanced manufacturing.
- Feasibility and clarity of research plan, including data management and postdoctoral mentoring (if applicable).
- Applicant must be a U.S. academic institution or eligible U.S. organization (independent researchers without U.S. affiliation are ineligible as PI).
Past Winners / Cohort Profiles
Past awardees are typically U.S.-based faculty or research scientists at universities (e.g., University of Michigan, Georgia Tech, University of Texas) working on topics like AI-driven water quality monitoring, nanomaterial-based toxin sensors, or life-cycle assessment of critical mineral recycling. Named examples are not listed on the page, but the program funds fundamental engineering projects with strong computational or process-engineering components.
Ideal Candidate Fingerprint
A U.S.-based early-career or established engineering professor with a track record in chemical, environmental, or resource engineering, proposing fundamental research that uses AI or computational modeling to improve resource efficiency, detect contaminants, or enable circular economy processes. The ideal applicant has a host institution that can submit the proposal and a clear plan for broader impacts, including partnerships with industry or government.
Recommended Framing
Eniola should partner with a U.S. academic collaborator (e.g., at University of Michigan or Georgia Tech) who can serve as PI and submitting organization. His CCT model and platforms (IMPRINT, TOPOLOGIX) can be reframed as fundamental engineering research on AI-driven detection of neurotoxins and pathogens in water/soil, leveraging his computational pharmacology and TDA skills to develop novel sensors or risk-assessment tools for environmental health, directly aligning with EWRE's interest in AI modeling and health safety.
Watch Out
Eniola is not affiliated with a U.S. institution and cannot be PI or submit directly; he must find a U.S. academic collaborator willing to host the project. The program does not fund independent researchers outside the U.S., and his background in neuroscience/pharmacology may be seen as tangential unless explicitly reframed as environmental engineering. No explicit early-career or LMIC track exists.