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The University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 11 days ago
equivalent doctorate (e.g., Sc.D., M.D.) Preferred Education Skills and Experience: Ph.D. in design-related discipline, specializing in deep learning and biometrics. A strong background with evidence of
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knowledge of Efficient Learning for computer vision Coding Skills: Familiar with any of the major deep learning libraries, including Pytorch We regret to inform that only shortlisted candidates will be
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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for a Arcus Foundation funded project entitledGibbon acoustic monitoring Lao PDR. The primary duties will be to develop a deep learning-based call detector for gibbons from acoustic recordings
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degree in mathematics, strongly encouraged to apply. Experience and demonstratable knowledge in deep learning and one or more of the following: transformer networks, implicit neural functions, graph neural
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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the WATOC Dirac Medal. As the successful candidate, you will contribute to the design and implementation of AI models that integrate quantum mechanical theory with deep learning, enabling rapid and
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating or testing deep learning models
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control using deep learning. Implement and test new algorithms in actual robot platforms. Job Requirements: PhD in Electrical and Electronic Engineering or related field. Hands on research experiences in