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guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Independently and collaboratively lead field
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materials within the uranium fuel cycle. Research topic areas will be related to understanding the effects of material processing on chemical and physical observables and translating this understanding
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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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. 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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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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delivering solutions to pressing energy storage problems essential to economic develop and security of the United States. As part of our research team, the candidate will be expected to work across a variety
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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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pipelines for structured and unstructured data Experience with multimodal datasets (e.g., imaging, time-series, and process data) Experience with API-based data services, workflow automation, or integration
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responses to environmental change. This position resides in the Ecosystem Processes Group in the Environmental Sciences Division at Oak Ridge National Laboratory (ORNL). The selected candidate will work with
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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