Research Associate Professor​/Research Professor & Assistant Director, Artificial Intelligence

Research Associate Professor/Research Professor & Assistant Director, Artificial Intelligence

Prairie View A&M University

Prairie View, Waller County, TX.

The Cooperative Agricultural Research Center (CARC) at Prairie View A&M University seeks qualified candidates for the Research Associate Professor or Professor and AI in Agricultural Research Leader position. This pivotal role is designed to advance the Artificial Intelligence Research Program by implementing innovative and strategic methodologies that address immediate and long-term challenges. The focus will include resolving critical issues concerning providing nutritious and healthy food for the world’s expanding population.

The incumbent will spearhead a distinguished AI research team supporting underserved producers and businesses by developing and applying safe, productive practices. This role will involve pioneering efficient, innovative, evidence-based solutions within agriculture, food systems, nutrition, and environmental management.

The research will incorporate advanced technologies such as high-resolution sensing systems and autonomous robotics to improve precision and operational efficiency in agricultural processes. Furthermore, sophisticated data analytics and machine learning algorithms will extract actionable insights from large and complex datasets, enhancing predictive capabilities and optimizing decision-making. The incumbent will integrate AI-driven technologies into climate-smart solutions that empower underserved communities and foster their development.

By leveraging state-of-the-art AI methodologies with sensing technologies, robotics, and data analytics, the role will advance food and agricultural systems, thereby improving all Americans' health, nutrition, and quality of life while contributing to global food security. The incumbent will also build a research team focused on innovative innovations that enhance food safety, address food insecurities, and promote nutritional security.

• Precision agriculture (machine learning, computer vision, remote sensing)
• AI-driven pest and disease management (neural network-based early detection)
• Automated irrigation and nutrient optimization (dynamic scheduling, predictive analytics)
• AI applications in animal science (predictive models, advanced monitoring)
• AI innovations in food science (real-time monitoring, predictive analytics)

These research areas provide the incumbent opportunities to engage in innovative projects addressing critical challenges and driving innovation across agriculture, animal science, and food science.

The AI in Ag System Leader, in collaboration with the Executive Associate Director of the CARC, the Assistant Director of CARC, and the Associate Dean for Academic Programs, will establish the overall vision and oversee the operational management for the AI in Ag system regarding research priorities and the development of research scientists, postdoctoral fellows, research specialists and technicians, and student research interns within the Plant Systems.

As the chief administrative officer of the AI in Ag System Research Program, the leader has the responsibility for the delivery of a robust program of research and related activities by CAFNR and the CARC regarding mission goals and objectives as spelled out in the PVAMU, CAFNR, CARC, Cooperative Extension Program (CEP) and USDA/NIFA strategic plans. This position will involve the day-to-day management of staff and resources and operational strategies for the unit.

The position will support the work of AI in Ag System research scientists who are engaged in the growth of plants in field plots and greenhouse settings. It is pivotal for advancing research, education, and innovation in AI in Ag.

This position is funded by a grant or restricted funds. Continued employment is contingent on the renewal of grant or restricted funding.

• Develop advanced AI solutions: create and implement sophisticated AI algorithms and models to enhance food production and animal systems in agriculture, including predictive modeling, optimization techniques, and decision support tools.
• Data acquisition and analysis: gather and examine extensive datasets related to animal and food systems, covering livestock performance, environmental conditions, and market trends. Apply statistical analysis and machine learning methods to derive significant insights from the data.
• Enhance livestock management: utilize AI methods to refine livestock management practices, such as optimizing feed and nutrition, detecting diseases, and improving breeding programs. Develop predictive livestock health and performance models for early intervention and proactive management strategies.
• Optimize agricultural economics: collaborate with stakeholders in agricultural economics to develop AI-driven solutions that enhance economic efficiency and sustainability. This includes demand…