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capabilities in evaluating physical and chemical properties of environmental samples including plants and soil. Experience integrating phenotypic or imaging-based datasets (e.g., RGB, chlorophyll fluorescence
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Postdoctoral Research Associate - Multifunctional Equipment Integration Energy Conversion Technology
research assignments related to energy conversion systems including: Prototype development Material synthesis and analysis Performance testing Analysis of results Preparing research publications Prepare
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and aspects related to safety and best practices. The candidate will make extensive use of state-of-the-art imaging, spectroscopy (Raman, electron microscopy, infrared, etc.) and scattering methods
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time-of-flight secondary ion mass spectrometry (ToF-SIMS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). Experience in data reduction of big spectroscopy, mass spectrometry, and image
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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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research. Your work will focus on developing selective chelation strategies and applying these systems to targeted radionuclide therapy and cancer imaging. Research accomplishments will be disseminated
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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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on diverse, high-impact problems and to shape the future of intelligent manufacturing. Major Duties/Responsibilities: Develop and integrate imaging and other sensing modalities for data collection and
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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