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, and measure success. Basic Qualifications: A PhD in materials science and engineering, mechanical engineering, aerospace engineering, polymer science, or a related discipline completed within the last
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superconducting RF structures, high-power and low-level RF, high-voltage converter modulators, DC and pulsed power systems, the Central Helium Liquifier (CHL), Cryogenic Moderator System (CMS), vacuum systems, and
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Experiment (MPEX). This position will reside in the ORNL Fusion Energy Division (FED), reporting to the Division Director, and include significant interfacing with the Nuclear Structural Materials Group in
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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science, mechanical engineering
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Engineering, Nuclear Engineering (focus on Materials), or a closely related field. A minimum of six years of research experience post PhD in relevant field. Experience in at least one leadership position within
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. Research will involve growth of single crystals and measurements to understand their structural and physical properties including magnetism and thermal transport, as well as helping to identify new magnetic
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, engineering, business, or a related discipline. A minimum of 8 to 12 years of aligned professional experience, or a MS degree with a minimum of 7 to 11 years of relevant and aligned experience, or a PhD degree
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for multiple projects, develops work breakdown structures, Project Management Plans and/or backlog feature and story definitions, project tasking and sequencing for project schedules, develops project budget and
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of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and