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deep learning, computer vision, or generative AI. Demonstrated experience with one or more deep learning frameworks (e.g., PyTorch, TensorFlow). Demonstrated ability to work with large-scale Earth
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, and communicate the center’s capabilities and vision. Internally align with ORNL leadership, HPC facility management, ORNL’s National Center for Computational Sciences, data services groups
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with one or more of the following models: WRF -Hydro, Advanced Terrestrial Simulator ( ATS ), Stormwater Management Model ( SWMM ), Hydrologic Engineering Center – Hydrologic Modeling System ( HEC
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remote sensing, large-scale mapping, geospatial analytics, Earth system science, real-time geospatial streaming, or associated research infrastructure. Experience leading or being part of a research
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Student Assistant (f_m_x) - Modeling mantle flow, true polar wander and core-mantle boundary heat...
and approx. 500 guests - work in a multidisciplinary way and with a unique infrastructure to research the dynamic processes of the Earth system. Our vision: “Taking the pulse of the Earth to safeguard
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prompt templates. Design and implement AI algorithms and systems, including but not limited to neural networks and machine learning, computer vision, intelligent decision-making, recommender systems
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generally suitable for candidates seeking part-time employment. Please indicate your request in your application. Position#5: TRR404-TUD-B06 Project: B06 System Models, Architectures, and Application Mapping
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Vacancies for Academic Staff On Monday 17 August 2026, from 20:00 untill 22:00, you will not be able to apply online or complete your application as the system is undergoing maintenance. On this
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work and leadership, reflecting established excellence in teaching, mentoring, and/or research. AI + Materials Department of Materials Science and Engineering, College of Earth and Minerals Science