315 application-forms-"https:"-"https:"-"https:"-"UCL" positions at Oak Ridge National Laboratory
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manufacturing of polymer composites for industrially relevant applications Lead research in composite joining technologies, with emphasis on ultrasonic welding and advanced joining methods Support scale-up
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. The facility contains over 10000 square feet of cleanroom space for separations and sample preparation. In addition, there are higher level laboratories across campus for various applications. The separation
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occasional remote contractors which help to support the group and overall NCCS mission. Strategically advance technical professional subject matter expertise for a variety of technical projects, programs
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polymerization, as well as macromolecular characterization. Nanofabrication Research Laboratory — Develops methods to fabricate nanostructures using best-in-class lithographic, etching, thin-film deposition, and
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networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The position is supported by
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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 choice for employment. These principles
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test facilities. The Section Head leads technical groups, setting technology and operational directions, supporting world-class achievements, ensuring clear project priorities, modeling exemplary safety
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development, fostering a safe and inclusive work environment, and maintaining focus on priority tasks. Build and sustain world-class technical expertise within the group by recruiting top talent, fostering
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are helpful. Experience with safe forest pesticide applications and efficacy assessment. A TN Pesticide Certification would be helpful. Working knowledge of dendrology and silviculture. Familiarity with forest
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material