80 parallel-and-distributed-computing-phd-"Multiple" positions at Arizona State University
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. Reporting to the University Librarian, the AUL for Special Collections’ portfolio includes librarians, archivists, and staff distributed across these units: Community Driven Archives (CDA) Initiative
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develop and teach undergraduate courses in digital/computer forensics. Faculty Associate positions are one-semester, non-benefits-eligible, fixed-term appointments with no tenure implications, not to exceed
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system informed by scientific evidence, and in which investigative and legal processes reflect empirically supported best practices and all people receive fair treatment. Our psychology and law program
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system informed by scientific evidence, and in which investigative and legal processes reflect empirically supported best practices and all people receive fair treatment. Our forensic science program
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on collections and services in the medical environment and prepared to collaborate with other members of the ASU Library community to provide holistic support for students in the dual program. We will place a
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Required Qualifications: ●ALA-accredited Master’s degree in Library and Information Science by the hire date ●Experience working with special collections or archival materials ●Experience with
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Required Qualifications: ●ALA-accredited Master’s degree in Library and Information Science by the hire date ●Experience arranging, describing, and preserving archival collections ●Experience with
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Gain exposure to the work and culture of an innovative academic research library. The ASU Library Internship Program provides current MLS students and recent grads with a unique platform for
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-collected data for the purpose of generating new insights about psychological processes Publish high quality research in the fields of machine learning, computational linguistics, cognitive psychology, and/or
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performance. The salary is commensurate with experience. Applications are invited from individuals who are interested in applying experimental psychology and Bayesian computational modeling to understanding