Research Assistant/Associate Professor (Agro-Ecology)

Texas A&M University System • Prairie View, TX

1d • Onsite

About The Position

The Research Assistant/Associate Professor in Agro-ecology will lead advanced research and project initiatives focused on natural resources and environmental sciences (NRES) and agricultural systems. The position requires expertise in using agro-ecological data and applying data analytics techniques to evaluate the effects of extreme weather events, anthropogenic activities, and soil amendment technologies on soil health, crop productivity, and agricultural greenhouse gas emissions. The successful candidate will be instrumental in developing smart agricultural strategies through innovative, data-driven solutions and interdisciplinary collaboration to enhance agricultural resilience and sustainability. This position may also involve teaching, advising students, and mentoring graduate research assistants. This position is funded by a grant or restricted funds. Continued employment is contingent on the renewal of grant or restricted funding. The salary is determined in accordance with the University’s compensation structure and will be commensurate with the candidates’ education and experience, within the assigned salary range for this position.

Requirements

• Ph.D. in Agro-ecology, Environmental Science, Earth Science, or closely related fields.
• Minimum three years of related research experience.
• A strong research background in agroclimatology, particularly in the analysis of agro-ecological variables, extreme-weather events projections, and the impacts of these extremes on agriculture.
• Proven track record of peer-reviewed publications in fields related to agro-ecology, soil health, natural resources and environmental sciences (NRES), and greenhouse gas analysis.
• Demonstrated success in securing external research funding, including serving as Principal Investigator (PI) or Co-Principal Investigator (Co-PI).
• Advanced knowledge and skill in analyzing agrometeorological data, greenhouse gas dynamics in agricultural systems, extreme-weather events projection, including downscaling techniques, and assessment of weather/meteorological extremes.
• Proficiency with programming languages and software commonly used in hydrological and agricultural modeling (e.g., Python, R, GIS, and other environmental and hydrological modeling software, e.g., SWAT, DHSVM).
• Ability to apply machine learning and artificial intelligence tools and techniques to address critical issues of agriculture and NRES.
• Ability to work with large datasets, including remote sensing data, and field measurements, to develop predictive models and simulations.
• Excellent communication skills, including the ability to write scientific publications, deliver presentations, and communicate research findings to a variety of audiences.
• Ability to collaborate effectively with interdisciplinary teams and external stakeholders, including government agencies, industry partners, and the agricultural community.
• Demonstrated ability to contribute to the University’s land-grant mission through integrated research, teaching, and outreach activities.
• Strong project management, organizational, and leadership skills, with the ability to manage multiple research projects and funding portfolios.
• Ability to establish collaborative partnerships and maintain effective professional relationships with students, faculty, and external stakeholders.

Nice To Haves

• At least five years of research experience in Natural Resources and Environmental Sciences, ecology, modeling, AI/ML and data science.
• Proven experience in securing extramural grant and leading research projects.
• Proven record of peer-reviewed publications in Natural Resources and Environmental Sciences (NRES), ecology, modeling, AI/ML and data science.
• Experience in teaching undergraduate and graduate courses.
• Background in Geospatial Technology, AI/ML and NRES research.

Responsibilities

• Lead and conduct research on agricultural greenhouse gas emissions, agro-ecological variables, soil health, crop productivity, climate extremes, soil amendment technologies, and mitigation strategies.
• Apply machine learning, statistical modeling, and data analytics techniques to evaluate and predict the effects of climate change, extreme weather, and soil management practices on agricultural systems.
• Develop and submit competitive grant proposals, establish industry and research partnerships, and secure external funding to support research initiatives.
• Prepare and publish research findings in peer-reviewed journals and present results at national and international conferences.
• Collaborate with multidisciplinary researchers, including climatologists, agronomists, data scientists, hydrologists, and policy experts, to develop data-driven strategies for climate-resilient and sustainable agriculture.
• Develop and teach undergraduate and graduate courses in agroclimatology, climate modeling, machine learning applications in agriculture, and related areas.
• Advise, mentor, and supervise undergraduate and graduate students, graduate research assistants, and other research personnel.
• Participate in departmental, college, university, professional, and project-related service activities, college-wide events and professional developments.
• Performs other duties as assigned.

Benefits

• The salary is determined in accordance with the University’s compensation structure and will be commensurate with the candidates’ education and experience, within the assigned salary range for this position.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree