132 web-programmer-developer-"https:"-"UCL"-"U"-"https:"-"https:" positions at Texas A&M AgriLife in United States
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Managers to coordinate and support efforts related to our portfolio of community-engaged research studies and projects. Support the development and implementation of local, state, and national health
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samples. Develop and conduct research projects related to a systems-wide approach to increase consumption of vegetables, fruits, and ‘foods for health’. Work on research teams and collaborate with other
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Sciences at Texas A&M University Texas A&M Forest Service Texas A&M Veterinary Medical Diagnostic Laboratory As the nation’s largest most comprehensive agriculture program, Texas A&M AgriLife brings together
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comprehensive agriculture program, Texas A&M AgriLife brings together a college and four state agencies focused on agriculture and life sciences within The Texas A&M University System. With over 5,000 employees
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the Texas Commercial Feed and Fertilizer Control Acts. Examine and review product labeling and documentation. Prepare, complete and submit technical communications such as inspection and investigation
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Job Type Staff Job Description Job Responsibilities ●30%: Laboratory Analysis and Treatment Evaluation - Conduct laboratory analyses of manure, lagoon wastewater, and treatment samples etc. Prepare and
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Type Staff Job Description The Project Coordinator II, under supervision, will provide technical expertise and management support for Fort Irwin's cultural resource management program. Responsibilities
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and Life Sciences at Texas A&M University Texas A&M Forest Service Texas A&M Veterinary Medical Diagnostic Laboratory As the nation’s largest most comprehensive agriculture program, Texas A&M AgriLife
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of funding sources (state, federal, private, etc.). Responsibilities: -Plan, coordinate, and execute field surveys as well as compile, analyze, and summarize results into scientific and clientele
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Type Staff Job Description 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