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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve
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preparation-related equipment. Instruct; train users (faculty, students, and postdocs) in the operation, and care of materials scientific equipment and provide scientific support regarding the theory behind
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equipment and giving instructions to others on how to use it Schedule Flexibility with work schedule Compensation Grade LOA https://www.unr.edu/hr/compensation-evaluation/salary-schedules/loa-and-postdoc
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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stimulation devices in collaboration with engineering and clinical teams. Data Collection & Analysis Acquire, process, and analyze high-dimensional electrophysiology and behavioral datasets. Use tools such as
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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more about AI at Princeton, please visit https://ai.princeton.edu
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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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willing and able to learn quickly within a collaborative and interdisciplinary team. Instructions on how to apply For more information and documents/templates/europass link, please visit https