A Biology Professor teaches foundational biology ideas, truths, and methods to students through a combination of classroom instruction, lab experience, and assignments at colleges, trade schools or other institutes. May assist or lead in the creation of the biology curriculum to be taught. In addition to instruction, they may assist or lead in research initiatives relating to their field.
Assistant Professor - Artificial Intelligence in Soil and Crop Sciences Texas A&M University - 4.3 College Station, TX Job Details Full-time | Tenure track 8 hours ago Qualifications AI models AI chatbots Computational research Reinforcement learning Developing new academic courses Robotic systems Experience working with graduate students Publishing papers in peer-reviewed journals AI platforms (beyond public GPTs) Computational modeling Machine intelligence Agricultural and food science research within academia Experience working with undergraduate students Advising undergraduate students Soil science Research assistant mentoring Simulation systems Machine learning (ML) fundamentals Teaching science AI-driven automation Senior level Manuscripts for peer-reviewed journals Remote sensing observations Sensors Research supervision Data analytics technologies Scenario analysis Precision agriculture technology
Full Job Description 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
