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New methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. Using modern sensors and data analysis techniques, it is now feasible to develop methods to predict plant growth and productivity based on information about their genome and environment. However, doing so requires expertise in plant sciences as well as computational sciences and engineering.

Through the Predictive Plant Phenomics (P3) Program, we bring together students with diverse backgrounds, including plant sciences, data sciences, and engineering, and provide them with data-enabled science and engineering training. The P3 NSF Research Traineeship (NRT) uses the T-training model proposed by the American Society of Plant Biology (ASPB) to provide students with training across a broad range of disciplines while developing a deep technical expertise in one area. The collaborative spirit required for students to thrive in this unique intellectual environment will be strengthened through the establishment of a community of practice to support collective learning.

The P3 Program will directly support 28 seven/year) trainees for their first year in the program. The courses developed in this program will be available to all students and we anticipate an additional 20 students (with funding sources other than the NRT) will complete all coursework requirements. This expertise, in combination with training in advanced communication and entrepreneurship skills, will enable our graduates to work across organizational and cultural boundaries as well as scientific disciplines.

About NSF NRT

The National Science Foundation Research Traineeship (NRT) is the Foundation's new traineeship program designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The NRT program also seeks to catalyze and advance cutting-edge interdisciplinary research, and prepare STEM graduate students more effectively for multiple research and research-related career paths.

 

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