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: Ph.D. in Computer Science, Computer Engineering, or a field closely related to the job duties of this position. Demonstrated research in one or more areas of HPC or AI (e.g., large-scale training
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computational and experimental campaigns, running in distributed large-scale advanced computing environments. The group also delivers AI-ready and FAIR scientific data, together with the orchestration, provenance
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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills
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, orchestrating large-scale investments and partnerships, and positioning ORNL as the national leader in geospatial HPC, data infrastructure, and emerging computing paradigms (edge compute, neuromorphic, quantum
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, root imaging) with biogeochemical, microbial, or environmental sensor data. Experience analyzing large datasets in reproducible formats (R, etc.) Demonstrated interdisciplinary and systems-level approach
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for Computational Sciences (NCCS). NCCS Provides state-of-the-art computational and data science infrastructure coupled with dedicated scientific and technical professionals tackling large-scale science and
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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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-to-end data systems or automated analytical workflows. Experience processing and analyzing large, complex datasets, including imagery, geospatial data, and operational or mission-relevant data sources
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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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, and other stakeholders to establish processes, promote consistency, timely follow-up, and data-informed decision making that support positive employe-employer relations. The successful candidate will