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/empirical analysis Technical capability in behavioural and organisational cyber security, and desirable experience applying AI or machine learning to cyber security contexts. Proven publishing ability across
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pathogens such as Japanese encephalitis and Rift Valley fever. Learning Objectives: The fellow will learn epidemiological techniques related to modeling parasitic and vector-borne diseases. Opportunities
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diseases such as Japanese encephalitis, Rift Valley fever, and related diseases. Learning Objectives: The fellow will have opportunities to learn field-based techniques related to survey and manage arthropod
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phenomena and impulsive events in the solar atmosphere. Our approach includes the implementation of machine learning models for multiline full-Stokes inversions. About the person: The successful candidate
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) in addition to Medicare and Medicaid claims. Our team also has extensive methodologic experience, including natural experiments/econometrics and various machine learning techniques. The Fellow will
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contract basis, renewable subject to performance and project needs. Project Title: Profiling Academic Trajectories and Heterogeneity of Low-Progress Learners for Enhancing Learning Support (PATHS) Project
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 16 hours ago
experience and interest in mental health, suicide prevention, health professions training, and/or military health, as demonstrated through any or all of the following: A record of scholarly publication and
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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datasets from laboratory experiments will be provided to support simulation and verification of the resulting model. Replicate and learn a theoretical model for wave and current interaction by posing
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phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring