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support research staff within ADADS and throughout ORNL to develop and apply modern data science/machine learning techniques to a wide variety of subjects including the characterization of nuclear material
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worldwide challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning
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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 values
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attitude, considering hazards for every task, and never stop learning. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety
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, organizational engagement, learning experience coordination, and cross-functional collaboration. The role will work closely with the HR Business Partners, managers, Office of General Counsel, Employee Concerns
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hazards for every task, and never stop learning. Delivering ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service
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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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management, and other needs for DOE, DOW, and DHS sponsors. This is an early-career position intended for a candidate who will work alongside and learn from experienced staff while independently executing
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learning Basic Qualifications: A HS diploma or GED A minimum of two years of commercial/industrial or sanitation janitorial experience or an equivalent combination of education and experience Preferred
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. Demonstrate proficiency in MS Word, MS Outlook, MS Excel, and PowerPoint. Must have ability to learn quickly and adapt to rapid changes. Excellent interpersonal and verbal and written communication skills