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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
. Responsibilities for this position includes:* Developing deep-learning approaches for insight and analysis about mouse behavior from video * Machine Learning: use your expertise in statistics and machine learning
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, computer-aided decision support systems Previous experience with using deep learning models (e.g., convolutional neural networks, autoencoders, transformers) for academic research Documented experience in
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, computer-aided decision support systems Previous experience with using deep learning models (e.g., convolutional neural networks, autoencoders, transformers) for academic research Documented experience in
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At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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degree (or near completion) in Electrical Engineering, Computer Science, or a closely related subject. Research experience in wireless communications and deep learning is essential. The project will also
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electronic health records (EHRs) from multiple UK hospital centres using advanced data analytics including machine learning, deep learning, and statistical techniques—with a particular emphasis on deep
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degree (or near completion) in Electrical Engineering, Computer Science, or a closely related subject. Research experience in wireless communications and deep learning is essential. The project will also
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), particularly deep learning, in order to develop more advanced AI technologies and to pioneer their applications in the natural sciences. For more information, please refer to the following website: RIKEN Center