25 chromosome-structure-imaging Postdoctoral positions at Oak Ridge National Laboratory
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. Experience working with satellite remote-sensing data such as Landsat, Sentinel, MODIS, SAR, LiDAR, or derived land-cover and vegetation product, and experience with in-the-cloud image processing Experience
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structure, and simulation of coupled difference or differential equations. Experience calibrating simulation models against sparse, indirect, aggregated, or otherwise limited observations. Familiarity with
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alloys and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in
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with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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this role, you will study buried interfaces, defects, and charge-transfer processes in vertically integrated semiconductor structures. The research will focus on low-dimensional and emerging materials
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Requisition Id 16540 Overview: We are seeking a Postdoctoral Research Associate who will develop and apply computational methods based on electronic structure theory and artificial intelligence
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. Knowledge of fiber manufacturing processes and structure-property relationships is a plus. Strong analytical and problem-solving skills. Excellent written and verbal communication abilities. Experience in
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distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
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., neutron imaging, SPRUCE hydraulic/thermal thresholds and scaling Produce and publish AI-ready datasets to the ESS-DIVE data archive and BER data lakehouse Develop AI pipelines for experimental ecophysiology