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the interactions among urban infrastructure and environment networks. This opportunity will have the ability to develop complex systems analytics models, optimization methods, and computational tools for real-world
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and abundance of fish and larger invertebrates, and Cornell is at the forefront of the application of these methods in lakes. We are now collecting data with automated systems using uncrewed drones
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workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML) methods to tackle complex biomedical challenges in
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) methods to tackle complex biomedical challenges in nutrition and health. This is a one-year full-time benefits-eligible position that may be extended for up to four more years, depending on performance
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qualitative research methods, experience in research operations, and a demonstrated ability to collaborate across multidisciplinary teams. The Postdoctoral Associate will help to transform and expand the way
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mediated by a predatory stink bug and the prey itself to develop methods to control Colorado potato beetle. This position will combine field and lab experiments of insect behavior, movement, abundance, non
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comparative genomics, chromatin architecture, gene expression, protein abundance, and metabolite profiling—combined with computational biology, machine learning, and advanced statistical methods. Supported by
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predatory stink bug and the prey itself to develop methods to control Colorado potato beetle. This position will combine field and lab experiments of insect behavior, movement, abundance, non-consumptive
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to the work of the NTO. Further, this program is meant to produce use-inspired basic and applied research with a strong AI component, driven by real-world education applications. Faculty mentors are expected
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quantitative analytic methods. Core responsibilities include: Conducting data analysis for ongoing projects. Developing independent and collaborative publications by accessing available longitudinal data