13 complex-network Postdoctoral positions at The University of Arizona in United States
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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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of ecosystems. The project aims to: Improve our understanding of what controls net primary production of ecosystems. Develop upscaling methods to estimate whole-ecosystem transpiration from local measurements
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transpiration of ecosystems. The project aims to: Improve our understanding of what controls net primary production of ecosystems. Develop upscaling methods to estimate whole-ecosystem transpiration from local
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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
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metabolomic studies. Complete the assigned work in time. Complete complex research projects at different stages (advanced through nearly completed). Formulate, troubleshoot and optimize research methods and
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undergraduate students to fabricate/characterize complex nanomagnetic heterostructures and explore novel light-induced phenomena using ultrafast spectroscopy and imaging techniques. Outstanding UA benefits
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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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the University of Arizona and relocations services, please click here . Duties & Responsibilities Conduct advanced, multi-disciplinary research. Develop, design, and conduct complex research projects. Formulate
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conduct complex research projects. Formulate research methods and develop research criteria. Foster collaborations within the Department and with other units across the University. Maintain accurate and
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nuclear systems and neutrino dynamics. Qutrit-based and hybrid quantum-classical approaches for many-body simulations. Tensor network and entanglement-based methods in many-body physics. Knowledge, Skills