125 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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complex projects and troubleshooting complex system issues and access problems. Ability to set priorities, learn new skills, and prioritize continuing professional development. Commitment to strong service
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continuous learning. Operational Excellence and Compliance Champion safety, security, and ethical research practices; ensure compliance with ORNL policies and ESH&Q standards. Maintain clear internal and
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning (AI/ML
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and prepare periodic summaries of equipment utilization, partner requests, response times, calibration status, lessons learned, and program improvement opportunities. Engineering measurement and
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employees in matters related to waste management and transportation programs. Benchmark and share lessons learned with other DOE/Battelle laboratories and relevant professional networks. Align behaviors and
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excellence. Provide timely notification, documentation, and reporting of incidents, equipment failures, environmental events, and operational occurrences. Promote continuous improvement, continuous learning
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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred
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. Preferred Qualifications Familiarity with techniques for AI-on-AI adversarial evaluation, including reinforcement learning-based adversarial testing setups. Expertise in designing systems that support red