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Biology Stanford Cancer Center Postdoc Appointment Term: Open-ended. Appointment Start Date: ASAP Group or Departmental Website: https://rogala.stanford.edu/ (link is external) How to Submit Application
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Appointment Term: Initially 1 year, renewable Appointment Start Date: Start date is negotiable. Group or Departmental Website: https://www.huttenhainlab.com/ (link is external) How to Submit Application
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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, sensor technologies, and data analysis for research focusing on automated health monitoring in dairy cows. About the project This postdoctoral position is part of a newly funded research project aimed
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group aims to bring fundamental changes in the way the sensors and electronic devices are developed using materials designed for sustainability, and resource efficient printed electronics on flexible
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 hours ago
and testing of photonic chips and integrating chips into larger instruments. Applications include remote sensors like microwave photonics radiometers, compact lidar systems. We are interested in
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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on computational and theoretical condensed matter physics. Areas of research include diagrammatic calculations, quantum Monte Carlo methods, density matrix renormalization group and tensor network states, and
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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staff position within a Research Infrastructure? No Offer Description This research project aims to develop a synthetic dataset generation technique to optimize the training of neural networks (NNs