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, geolocation and AI-enabled communication and networking systems. In these roles you will undertake research centered on the intersection of networking, communications, geospatial methods and machine learning
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Cornell University, Mui Ho Center for Cities Position ID: Cornell-CfC-PDA [#31331, WDR-00056029] Position Title: Position Type: Postdoctoral Position Location: Ithaca, New York 14853
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scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML) methods to tackle complex
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. Recognised Researcher position has been opened. The ideal candidate holds a master's-level background in robotics, AI or related fields, with strong Python/C++ skills and experience in machine learning
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
to lift 25 pounds. Must be able to push equipment carts between research locations. Special Instructions For information on UNC Postdoctoral Benefits and Services click here Quick Link https
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multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early-stage plant stress. This project
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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machine learning and network biology. Background in cancer research. Pay Range: $62,232.00 - $81,000.00 To apply visit https://academicjobsonline.org/ajo/jobs/31509 and submit a cover letter, full CV, and
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due