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to multi-task and work cooperatively with others. Writing and communication skills. Preferred Qualifications: Knowledge of computer models on watershed assessment and/or flood management. Knowledge
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complements the strengths of Texas A&M AgriLife Research unit at Corpus Christi in Digital Agriculture. The incumbent is expected to work with a team of transdisciplinary scientists to develop predictive models
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, modeling, or AI/ML approaches in agricultural or environmental research. -Proficiency in server administration, distributed computing, and cloud platforms (AWS, Azure, Google Cloud). -Knowledge of full-stack
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. The position will involve designing and analyzing experiments, modeling producer and consumer behavior, and integrating experimental and observational approaches to generate robust evidence. The postdoc will
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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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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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. Proficiency in computer programming, including scripting in Python, Fortran, or other computing tools for data processing and modeling. Experience in grant proposal writing within a collaborative environment
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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 brings
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largest most 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
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: Expertise in interpreting LC-MS, GC-MS, Raman spectroscopy, and NMR data. Experience in metabolic profiling and quantification of bioactive compounds in plants. Familiarity with computational modeling