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-Battelle offers a generous relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions. If invited to interview, be sure to ask your
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in a technical field (physical sciences, engineering, environmental discipline, or related) and 15 years of relevant experience in environmental management, waste management, hazardous materials
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), and ensuring compliance with ORNL and DOE project management governance. Conducting project management process and systems training for all relevant project staff, with a specific focus on technical
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systems (proteins, enzymes, membranes, and complexes) Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity Contribute to cross-scale modeling
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. This position resides in the Radiation Effects and Microstructural Analysis Group (REMAG) within the Materials in Extreme Environments Section of the Materials Science and Technology Division in ORNL’s Physical
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge
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(MS or PhD) in Computer Science, Data Science, Geospatial Science, Electrical or Mechanical Engineering, Physics, or a closely related discipline. Minimum 12 years of experience with a doctoral degree
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: BS/BA degree in engineering, physics, mathematics, chemistry, international studies, or related field and
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for the chelation and separation of metal ions relevant to isotope production and energy. This position resides in the Chemical Separations Group in the Chemical Sciences Division, Physical Sciences Directorate (PSD
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systems (proteins, enzymes, membranes, and complexes) Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity Contribute to cross-scale modeling