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ensuring compliance with university policies and applicable laws and regulations. Provide threat and risk assessment information to aid in event security planning for large campus gatherings and major campus
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& Computational Neuroimmunology (CTCN), in the Department of Neurology at Columbia University Irving Medical Center (CUIMC) is a hub for large-scale translational human studies that integrate rigorous application
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for events, prepares and disseminates announcements. Gathers and prepares information necessary for government grant annual and periodic performance reports for the MRSEC to the National Science Foundation
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supporting a living and learning environment: Advise Hall Council, providing direction and information on University policies and procedures. Serve as a partner in students? development, establishing
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. Additional responsibilities include: collecting/analyzing employment data, generating placement and related reports for the Department/School, assisting with the placement of BS students as needed, supporting
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mortality and informatics, with a specific focus on the inpatient labor and delivery and post-partum patient population. Research will include mixed methods, both quantitative modeling of EHR data and
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research projects, such as registries, retrospective data reviews, long-term follow-up studies, and other non-interventional studies. Responsibilities Clinical Research Responsibilities: Screen participants
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for licensing, across research fields such as bio, IT, clean tech, devices, big data, nanotechnology, materials science, and more. CTV has extensive experience founding and supporting technology initiatives
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priorities. They will partner with fundraisers by providing strategic information and fundraising intelligence that drives effective decision-making throughout the fundraising cycle. This individual must be
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and electroencephalography / event-related potentials. There is an opportunity to work with a dataset of 16,000+ patients, applying machine learning approaches. Therefore, familiarity with large data