14 computational-"https:"-"https:"-"https:"-"https:"-"Max-Born-Institute" Postdoctoral positions at Texas A&M AgriLife
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AgriLife and make a difference in the world! Position Information Texas A&M AgriLife Research – Dallas Center ( https://dallas.tamu.edu/ ) is seeking a highly motivated individual to join the
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Job Title Postdoctoral Research Associate Agency Texas A&M Agrilife Research Department Institute for Advancing Health through Agriculture-Project Management Proposed Minimum Salary Commensurate Job
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Job Title Postdoctoral Research Associate Agency Texas A&M Agrilife Research Department Institute for Advancing Health through Agriculture-Project Management Proposed Minimum Salary $5,333.34 monthly
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polyploids. Ability to effectively utilize computational systems and applicable software, including breeding databases, perform statistical analysis, present data, and perform other computer-related tasks
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Job Title Postdoctoral Research Associate Agency Texas A&M Agrilife Research Department Institute for Advancing Health through Agriculture-Project Management Proposed Minimum Salary $4,583.34 monthly
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EPIC, APEX, RUFAS, BCSM, GLEAM, or similar simulation frameworks. Proficiency in scientific programming (e.g., Python, Fortran) for data processing, model development, and computational analysis. What
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for breeding climate-resilient, high-quality horticultural crops. •Familiarity with statistical modeling and computational approaches for bioactive compound analysis. •Next-generation sequencing
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AgriLife’s Institute for Advancing Health Through Agriculture (IHA) Healthy Living program, based at the AgriLife Center at Dallas, seeks a Postdoctoral Research Associate to work in close
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supervision of Dr. Kiran Gadhave in the Gadhave Laboratory (https://tx.ag/gadhavelab) at Crop Stress Lab (Texas A&M AgriLife Research), Bushland, TX. Responsibilities: Independently lead research projects
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: • Experience with LC-MS, GC-MS, Raman spectroscopy, and NMR for compound identification and metabolic profiling. • Familiarity with statistical modeling and computational approaches for bioactive