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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
No Position Type Permanent Staff (EHRA NF) Working Title Learning Specialist Appointment Type EHRA Non-Faculty Position Number 20075080 Vacancy ID NF0009617 Full Time/Part Time Full-Time Permanent FTE 1 Hours
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-doctoral fellowship from IDMEC. Workplan and the objectives to achieve: The main objective of the work is to develop and optimize reinforced panels by combining dissimilar materials such as aluminum, steel
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and wound-healing applications, reinforcing cross-cutting NSF-type and NIH-type translational research pathways. Duties and Responsibilities: Primary responsibilities emphasize research development and
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expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon the successful completion of a background check. Our
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. Support student‑athlete academic success by reinforcing academic expectations and collaborating with campus support services. Provide position‑specific coaching and instruction to enhance technical skills
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artificial intelligence (i.e. machine, deep and reinforcement learning…) to optimize efficiency, improve safety, reduce costs and promote sustainability. Collaborate with multidisciplinary teams to uncover a
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provides outreach service to the community. Effectively administer assessment of student learning and technical objectives. Ensure safe and reasonable precautions are implemented to protect students
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, concepts, formats, scripts, visuals, subtitles, and workflow. You experiment (and learn) with tools to produce faster and better, without losing the brand style. Content planning and monitoring You develop
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the maintenance and coordination of clinic spaces, equipment, and supplies. Champion patient safety by reinforcing and educating on safety-related policies and procedures. Maintain compliance with all university
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under uncertainty (e.g., planning, reinforcement learning, probabilistic reasoning); Multimodal information fusion and state estimation; Foundational or representation learning models for robot