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responses of environmental systems at the environment-human interface and the consequences of alternative energy and environmental strategies. With the aim of enhancing national energy security
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. Basic Qualifications: PhD in electrical/computer engineering, computer science, or a related discipline A minimum of 8 years of relevant experience in image/signal processing and machine learning
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. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top
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science, and materials engineering, with emphasis on understanding material behavior in complex chemical and radiological environments. Research activities may include the design of functional nanomaterials
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at the environment-human interface and the consequences of alternative energy and environmental strategies. The Center for Radiation Protection Knowledge provides technical assistance to U.S. federal agencies involved
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challenging and impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. We have a collaborative environment focusing on designing
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, declassification, and classified subject areas: Bachelor’s degree plus 12 years of relevant experience, or Master’s degree plus 10 years relevant experience, or PhD plus 7 years relevant experience The applicant
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-controlled environment Demonstrated teamwork skills for coordinating work of multidisciplinary scientists and in collaboration with external stakeholders Project management skills to function well in a fast
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-flows. Basic Qualifications: PhD in mathematics, computer science, engineering, or related field earned within the last 5 years. Preferred Qualifications: Experience with mesh generation/CFD applications
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for construction-scale automation. Advance human-robot collaboration methods in manufacturing and construction environments. Integrate sensing, control, and manufacturing systems to enable repeatable, scalable