Tenure Track Assistant Professor Artificial Intelligence in Soil and Crop Sciences

Texas A&M University; College Station (TX); On-site; USD 100,000 - 150,000; Full time

Job summary

Texas A&M University’s Department of Soil and Crop Sciences in College Station, TX, invites applications for a tenure-track Assistant Professor position in AI in Soil and Crop Sciences. The role combines research, teaching, outreach, and service with a start date in August 2027.

The ideal candidate will lead AI-driven research across agronomy, environmental science, data analytics, and related fields, collaborating with diverse researchers on- and off-campus.

Qualifications

• Ph.D. or equivalent doctoral degree; ABD may be considered with progress toward completion.
• Strong knowledge and experience in AI and agricultural/environmental data.
• Desirable interdisciplinary collaboration and grant-writing experience.

Responsibilities

• Develop a highly impactful, extramurally funded AI research program in soil and crop sciences.
• Develop and teach two courses: intro AI in agriculture and a stacked undergrad/grad course on precision agriculture.
• Advise/mentor students, publish regularly in peer-reviewed journals, and engage in outreach and service.

Skills

Artificial intelligence

Machine learning

Data analytics

Education

Ph.D. or ABD in environmental/science/engineering

Job description

Position Description

The Department of Soil and Crop Sciences in the College of Agriculture and Life Sciences at Texas A&M University in College Station, TX, seeks outstanding applicants for a tenure-track Assistant Professor faculty position in Artificial Intelligence in Soil and Crop Sciences. This is a 9-month, full-time, tenure-track faculty position with research, teaching, outreach, and service responsibilities. This position is part of a four-position cluster hire in AI in Agriculture across the College of Agriculture and Life Sciences to develop an undergraduate minor in AI-Enabled Agricultural Systems and build research capacity in this area. The anticipated start date is August 16, 2027.

Major Duties and Responsibilities

The successful applicant will be responsible for developing a highly impactful, extramurally funded research program integrating artificial intelligence into soil and crop sciences. Their work would use artificial intelligence to improve knowledge and outcomes by leveraging and integrating varied relevant data (which may include agronomic, environmental, economic, genomic, nutrition, pest and disease, phenotypic, physiological, management, microbiome, soil, weather, and water), as well as applications and technologies (which may include precision soil and water management, decision-support tools, precision human nutrition, remote sensing, robotics, sensors and variable-rate applications). The successful applicant must demonstrate strong expertise in artificial intelligence, machine learning, data analytics, or related computational approaches, along with experience in applying these tools to soil, crop, environmental, or biological systems.

The individual will work closely with agronomists, computer scientists, crop physiologists, data scientists, engineers, genomics and genetics researchers, plant breeders, soil scientists and water researchers in Texas A&M AgriLife Research and the Texas A&M AgriLife Extension Service, both on and off-campus. Interdisciplinary collaborations with scientists and stakeholders in the region, nationally, and internationally is expected.

The individual will develop and teach two courses in the Department of Soil and Crop Sciences. One course is expected to be an introductory undergraduate course on artificial intelligence and its applications in agriculture and environmental sciences. Emphasis will be given to Large Language Models and their integration into chatbots and virtual consultants. The second course is expected to be a stacked undergraduate/graduate course with a focus on precision agriculture. One or both of these courses should include topics such as agentic AI for autonomous crop and pest management decision-making, foundation and multimodal models, human-robot collaboration for field operations, causal machine learning for interpretable agronomic and ecological modeling, digital twins for real-time crop system simulation and scenario planning, and reinforcement learning for management of cropping systems; or similar topics as they emerge. The individual will be expected to provide substantial leadership in developing a new certificate program in digital agriculture and AI applications in soil and crop sciences.

The individual will advise and mentor undergraduate and graduate students, postdoctoral scientists, and research technicians. They, along with their mentees, will be expected to publish regularly in peer-reviewed journals appropriate to the discipline. They will participate in outreach and service activities related to the position.

Distribution of Effort

60% research, 30% teaching, and 10% outreach and service

For further information, please contact the Search Committee Chair, Dr. Seth Murray at [e-mail]

Qualifications

Required Qualifications: Ph.D. or equivalent doctoral degree in environmental science, plant or crop science, agronomy, soil science, computer science, bioinformatics, mathematics, statistics, remote sensing, agriculture, biosystems engineering, electrical engineering, or related disciplines. Candidates who have completed all Ph.D. requirements except the dissertation (ABD) will be considered provided they demonstrate clear progress toward completion prior to the position start date. Strong knowledge and experience in both artificial intelligence as well as agricultural, soil or environmental data are required.

Desired Qualifications: Experience working with crops, field-based research, handling large datasets, interdisciplinary collaboration, sensing technologies and grant writing is desired. Additional qualifications include excellent oral and written communication skills, a good track record of publishing in peer-review journals, and teaching experience.

Salary will be commensurable with qualifications and experience.