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, and compliance requirements. Strong aptitude for computer systems, electronic tools, and digital workflows. Ability to learn and adapt to new technologies, including AI-enabled tools used to support
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, or office support experience (or equivalent combination of education and experience). Proficient in office computer systems and software applications such as Microsoft Office/Outlook. Must work well in a team
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needs. By leveraging advanced simulations, machine learning, and data-driven insights, the group enables more effective operations aligned with evolving energy demands. The group also develops hydrologic
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modeling, multiscale approaches) to support materials development and manufacturing process understanding. Use AI, machine learning, and data-driven methods as enabling tools to accelerate experimentation
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modeling, machine learning, and automated experimentation. Mentor and support Group Leaders to ensure excellence in research performance, staff development, inclusion, and cross‑disciplinary collaboration
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, assessing hazards for every task, and committing to continuous learning. Other tasks as assigned by management. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values
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, JAX etc.) Two or more years of experience in applying machine learning methods for instrument control, such as on a microscope, or on a nanomaterials synthesis platform resulting in publishable
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research, operations, and community engagement, and work cooperatively to leverage scientific capabilities across ORNL. Work in a highly collaborative environment with data scientists, machine learning
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Analysis and Machine Learning Group. This group focuses on scientific computing with a strong emphasis on scientific machine learning and data analysis. We are specifically interested in applicants with
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to a general science-interested audience as well as a more scientifically trained audience such as researchers and sponsors. Should also have a firm grasp of standard business computer software (e.g