Postdoctoral Research Associate

Texas A&M University System • College Station, TX

21h • Onsite

About The Position

Major/Essential Duties of Job: 1. Develop machine learning or physical based models for plant water stress quantification. 2. Develop machine learning models for crop mapping and management. 3. Prepare and publish articles in national/international peer-reviewed scientific journals that highlight findings from the research projects. 4. Present research findings at national/international events and conferences; 5. Collaborate with various, relevant faculty at the Texas A&M AgriLife Research in developing grant proposals for increased future funding, preparing peer-review journal articles, and supervising graduate students, if any. 6. Performs other duties as assigned. Required Education: A PhD degree in a field related to the tasks above within the broader theme of remote sensing, computer science, or ecology with a quantitative, and empirical orientation. Required knowledge, skills and abilities: Ability to multi task and work cooperatively with others Good communication and computer skills Please attach Cover Letter, CV/Resume, Transcript, References & other documents. Any questions, please contact Dr. Wenzhe Jiao @ [email protected] Continued employment in this position is contingent upon availability of funds. All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution’s verification of credentials and/or other information required by the institution’s procedures, including the completion of the criminal history check. Equal Opportunity/Veterans/Disability Employer. As the largest comprehensive agriculture and life sciences program nationally, Texas A&M AgriLife brings together a college and four state agencies within the Texas A&M University System With over 5,000 employees, and a presence in every county across the state, Texas A&M AgriLife is uniquely positioned to improve lives, the environment, and the economy of Texas and the nation through education, research, extension and service. If your goal is the pursuit of excellence and your vision is to make a difference in this world, we invite you to explore our opportunities to discover what a career at Texas A&M AgriLife has to offer you. Texas A&M AgriLife is comprised of the following Texas A&M University System members: Texas A&M AgriLife Extension Service Texas A&M AgriLife Research College of Agriculture and Life Sciences at Texas A&M University Texas A&M Forest Service Texas A&M Veterinary Medical Diagnostic Laboratory Texas A&M AgriLife collaborates with Prairie View A&M University, Tarleton State University, Texas A&M University-Commerce, Texas A&M University-Kingsville and West Texas A&M University as well as with other public and private partners. Prospective employee benefits Retirement programs Commitment to inclusive excellence Strategic plan

Requirements

• A PhD degree in a field related to the tasks above within the broader theme of remote sensing, computer science, or ecology with a quantitative, and empirical orientation.
• Ability to multi task and work cooperatively with others
• Good communication and computer skills

Responsibilities

• Develop machine learning or physical based models for plant water stress quantification.
• Develop machine learning models for crop mapping and management.
• Prepare and publish articles in national/international peer-reviewed scientific journals that highlight findings from the research projects.
• Present research findings at national/international events and conferences
• Collaborate with various, relevant faculty at the Texas A&M AgriLife Research in developing grant proposals for increased future funding, preparing peer-review journal articles, and supervising graduate students, if any.
• Performs other duties as assigned.

Benefits

• Prospective employee benefits
• Retirement programs
• Commitment to inclusive excellence
• Strategic plan

What This Job Offers

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