Back to Listings Tenure-Track: Assistant Professor, Artificial Intelligence for Horticultural Crop Systems Horticultural Sciences Location College Station, Texas Open Date 6/24/2026 Position ID 185014 Description The Department of Horticultural Sciences in the College of Agriculture and Life Sciences at Texas A&M University invites applications for a full-time, 9-month, tenure-track Assistant Professor position in
Artificial Intelligence (AI) for Horticultural Crop Systems. This position is part of a four-position cluster hire in AI in Agriculture in the College of Agriculture and Life Sciences aimed at building new research capacity and supporting the development of an undergraduate minor in AI-Enabled Agricultural Systems to prepare students for a rapidly expanding ag-tech workforce. Within this cluster, the Horticultural
Sciences position focuses on applying artificial intelligence to address core biological and production challenges in horticultural crops, including but not limited to variable environments, crop load balance, water use, and disease pressure. The position is grounded in plant biology, with an emphasis on understanding and modeling plant growth, yield, quality, and stress responses across a wide range of horticultural
productions systems ranging from annual crops to long-lived perennials and tree crops, and integrating plant, environmental, and management processes. This position focuses on the development of models that connect horticultural crop processes with production outcomes and support both discovery and application. These models will improve understanding of plant performance across environments while informing key
management decisions such as irrigation scheduling, harvest timing, crop load management, and responses to biotic and abiotic stress. Model development will be supported through the integration of diverse data sources, including but not limited to environmental sensing, canopy imaging, biosensors, genomic data, and image-based phenotyping, combined with machine learning methods that leverage large-scale datasets.
These efforts will lead to the development of decision-support systems for horticultural crop production that improve management efficiency, enhance resilience for existing producers, and lower barriers for new entrants to adopt horticultural systems. We are seeking candidates with a strong background in horticulture, plant sciences, or crop systems who also have expertise in artificial intelligence, machine learning
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