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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
the ability to design and implement novel artificial intelligence algorithms. Examples of research directions could include: multimodal foundational models for biomedical data, deep-learning architectures
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blotting), and diverse modalities of optical microscopy and radiologic imaging. Candidates who demonstrate scientific curiosity, passion for research, and deep knowledge of the research literature will
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information, remote sensing data, and GIS software. Experience in deep learning and computer vision. Experience in developing software tools and products. Experience in writing scientific papers and successful
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characterization of defects in AlN and high-Al content AlGaN. Deep knowledge of semiconductor physics. Keen interest and/or experience in application of AI approaches. Knowledge of ultrawide bandgap semiconductors
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. Topic / Area: The positions are flexible in terms of research direction within 3D vision and graphics with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in
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-reconstructions and observations, low-order data assimilation, or deep neural networks. A quantification of the impact of mesoscale and submesocale features is also expected. At a later stage, the successful
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diffraction (XRD) and Rietveld refinement. Hands-on experience with electrochemical characterization techniques (CV, EIS, GITT, galvanostatic cycling). Deep understanding of Li-ion battery cathode materials
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responsible for building a simulator that allows researchers to see the response of their proposed mutations at DNA, RNA and protein levels. The candidate should have a deep understanding of the central dogma
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knowledge of X-ray diffraction (XRD) and Rietveld refinement. Hands-on experience with electrochemical characterization techniques (CV, EIS, GITT, galvanostatic cycling). Deep understanding of Na-ion battery
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of LLRNs is therefore a key issue in guaranteeing the long-term safety of deep geological waste disposal. Quantifying long-lived radionuclides generally involves chemical separation steps to remove