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in Epidemiology with other terminal degree (PhD, DrPH) in related field. Experience: Experience in teaching undergraduate and graduate course in epidemiology. Responsibilities: Successful candidate
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fall and spring semester, the Lecturer will teach at most three sections in our core Computer Science curriculum, including introductory and advanced programming, computer systems, computer algorithms
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-machine interface (to include augmented and virtual reality [AR/VR]), human-systems integration, and augmenting human performance Computational neuroscience and its links with machine learning and
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bi-parental mapping analysis. Machine Learning and Statistical Analysis: Proficient in conducting and troubleshooting machine learning analysis using large image or numerical datasets for disease
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-robot collaboration, human behavior modeling, human-robot communication, learning and cognition, planning and re-planning, management of trust and its effects on collaboration, and forensics for human
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machine learning. We seek candidates who use data as a core element of their research and have a proven track record (for the Associate Professor rank) or a demonstrated interest (for the Assistant
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statistical analysis approaches, including time-series forecasting, machine-learning, multi-level modeling, and spatial econometric techniques. Prior experience of employing these approaches on research about
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analytical techniques and machine learning. A primary task of the post-doctoral researcher will be to lead model development and enhancement efforts within the UMD NCSG-CGIS team. The goal of this project is
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, the Internet of Things, Machine Learning, and use-inspired AI A list of all courses taught by the department can be found at https://go.umd.edu/doit-courses . Course descriptions can be found at: Undergraduate
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communication, learning and cognition, planning and re-planning, management of trust and its effects on collaboration, and forensics for human-machine teaming. We recognize that strength comes through diversity