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careerready to meet the world’s challenges, and that our faculty research and creative activity contribute to the public good. We achieve this through robust collaboration across departmental boundaries, with
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 23 minutes ago
accepted at this time, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Questions about this
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publications, conference presentations, and robust coding practices, including version control and reproducible data pipelines. Proficiency in Python, ML frameworks such as TensorFlow or PyTorch, statistical
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and maintaining a robust clinical finance reporting and analytics framework that focuses on financial integrity, customer service, monthly/quarterly financial reporting, coding and billing reviews
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factories, ii) designing tunable precision toggle switches for the controlled regulation of these shifts, and iii) developing toggle-switch-based inducible yeast strains for efficient biomanufacturing
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support the Office Coordinator. - Monitor and support immunization compliance for admitted students. - Maintain rigorous quality controls to ensure 100% accuracy. - Ensure compliance with Duke’s record
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policy interpretation. Guide proposal development (budgets, justifications, biosketches, facilities/resources), ensure compliance (IRB, IACUC, COI, IBC, export controls), and route through SPS to ORA/ORS
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, manufacturing) by creating applications for critical systems, adaptive and autonomous systems, advanced perception, diagnostics, quality control, and prediction systems. Further research areas include precision
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- THE ROLE – The purpose of the role is to support the delivery of robust financial planning, monitoring and reporting to ensure the organization uses resources effectively. The post holder will
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hardware constraints, improve system control, and unlock new modes of problem-solving that surpass classical approaches. The ML-QSIM project is built upon a robust multi-node and multi-regional structure