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Postdoctoral Research Associate to join the Microbial Engineering Group. In this role, you will develop next‑generation genetic tools for non‑model microorganisms, enabling precise genome engineering in
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–experiment (ModEx) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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AI to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape ecological, hydrological, and geomorphological responses to abrupt
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or expected to be completed within the next six months. Experience in ecological modelling and/or Land Surface Modelling Knowledge and experience working in HPC environment. Programming experience in Fortran
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or expected to be completed within the next six months. Experience in ecological modelling and/or Land Surface Modelling Knowledge and experience working in HPC environment. Programming experience in Fortran
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– in how we treat one another, work together, and measure success. Basic Qualifications: PhD (completed by start date) in plant ecophysiology, plant biology, ecology, Earth system science, or related
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to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Benefits at ORNL: UT Battelle
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to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Benefits at ORNL: UT Battelle