106 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" positions at Texas A&M AgriLife
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is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of AgriLife and
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computer and applicable software to maintain notes, populate databases, perform statistical analysis, present data and perform other computer related tasks. - Basic computer skills (Excel, Word, PowerPoint
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is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of AgriLife and
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is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of AgriLife and
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is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of AgriLife and
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here to learn more about how you can be a part of AgriLife and make a difference in the world! Position Information We are seeking a Research Specialist to contribute to research focused on improving
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is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of AgriLife and
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computer skills with knowledge of Microsoft Office and willingness to learn other programs as needed. Enthusiastic, highly motivated, and independent. Ability to multi-task and work co-operatively with
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Knowledge, Skills, and Abilities: Knowledge of laboratory maintenance and equipment. Knowledge of computers and computer software related to the position. Good interpersonal skills, professionalism and
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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