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Field
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Postdoctoral Research Scholar in Machine Learning and Computational Genomics Department of Epidemiology, School of Public Health, University of Pittsburgh The Department of Epidemiology
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applying advanced computational and experimental approaches to address complex environmental and infrastructure challenges. The postdoc’s work directly supports high-impact research outcomes, student
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systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job
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presence of complex landscape elements (lakes, forests, and wetlands) that typify the upper Midwest, this project will be focused on the Marcell Experimental Forest (MEF) in north, central Minnesota. The MEF
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limited to artificial intelligence, computing theory (algorithms, complexity), data science, statistics, discrete mathematics (graph theory, combinatorics), game theory, machine learning, optimization
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neuroethology and robotics. This position focuses primarily on the neuroethology aspects of our research program, with particular emphasis on understanding the behavioral and neural mechanisms underlying active
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, Outpatient, Carrier, TAF). Develop reproducible code and workflows for data cleaning, linkage, and analysis within Stanford’s secure computing environment. Collaborate with multidisciplinary teams
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proposal development and preparation of high-quality publications in top computer security, privacy, embedded systems, sensing, and networking venues. ● Pursue research topics such as protecting
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Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments. Modeling dynamical systems
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candidate will have previous basic science laboratory experience including rodent handling and colony management, rodent survival surgeries, auditory function testing, neurophysiology, microscopy, complex