The mission of AI2EAR is to enable long-term global food security and safety by accelerating collaboration between all the necessary disciplinary thought leaders and provide a framework for the sharing of critical resources and information needed to drive innovation.

The International Collaboration to Accelerate Integration of Engineering, Plant Sciences, & Agricultural Research using Artificial Intelligence, AI2EAR, is excited to announce the mini-grant award designed for early career professionals interested in traveling to and conducting research in a AI2EAR participating lab or attending conferences. Don't miss out on this fantastic chance to further your career and engage with our collaborative community!

​

What the mini-grant is funding?

• Up to 3 days of funding for travel to conferences, seminars, and workshops;

• 1 to 3 months of funding for travel and housing to a network member laboratory for research and mentorship;

​

Who can apply for the mini-grant?

Undergraduate, graduate, and post-doctoral students and assistant professors who are seeking to engage with the AI2EAR collaborative community.

​

When to apply?

The AI2EAR mini-grant works in ongoing application.

​

AI2EAR Research Objectives

​

• Sensor Science, Engineering, and Integration- (use plant-microbiome data to profile crop, soil, and environmental properties across scale.)

• Data Mining, Machine Learning, Multi-scale Modeling, and AI models (use novel analytical paradigms for processing heterogeneous and unstructured crop, soil, and environmental datasets to provide stakeholders with an agro-ecosystem decision support framework that captures complex interrelationships between crops and the evolving agricultural environment.)

• Open-source Data and Network Cyberinfrastructures (use data and network cyberinfrastructure that support innovations in sensor science and intelligent data mining analytics and will form the foundation of an open-source information sharing platform for real-time querying, systematic organization, and dynamic abstraction of heterogeneous agro-ecological datasets.)