15 software-defined-network-postdoc Postdoctoral positions at The University of Arizona
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at Biosphere 2. Together with an interdisciplinary team of scientists, the Postdoc will develop upscaling methods to estimate ecosystem scale transpiration based on locally observed energy balance partitioning
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) located at Biosphere 2. Together with an interdisciplinary team of scientists, the Postdoc will develop upscaling methods to estimate ecosystem scale transpiration based on locally observed energy balance
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funding and contribute to lab-based grant proposals. Minimum Qualifications PhD or MD conferred by the start date and 0-2 years of experience at Postdoc rank. Preferred Qualifications FLSA Exempt Full Time
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that influence volatile organic compound (VOC) cycling in soil. The Postdoc will track the fate of VOC carbon soil incubations in the laboratory and plant rhizospheres in growth chambers. The Postdoc scholar will
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research as a graduate student or early-level postdoc. Please note that U.S. citizens or green card holders will receive top preference due to eligibility for NIH postdoctoral fellowships. However
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of this position will be development of custom neural networks for functional annotation of protein sequences. This is an Extended Temporary Employment (ETE) position. Outstanding UA benefits include health, dental
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parity-check codes and iterative decoders, investigating applications of quantum error correction in quantum computers and networks. We are interested in candidates with strong expertise in burst error
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Qualifications PhD or MD conferred by the start date and 0-3 years of experience at Postdoc rank. Preferred Qualifications FLSA Exempt Full Time/Part Time Full Time Number of Hours Worked per Week 40 Job FTE 1.0
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. Knowledge of upstream, downstream, and centerline construction methods, and associated risks and regulatory frameworks. Proficiency in spatio-temporal modeling and geotechnical software (e.g., FLAC, GeoStudio
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statistical approaches. A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including the ability to design and implement custom NN models in PyTorch, as