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Field
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research, especially deep learning literature, and how these methods apply to our use cases Ability to manage multiple projects and assignments with a high level of autonomy and accountability for results
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in signal representation/processing, esp for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state
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Responsibilities: To conduct independent research in deep learning theory. To produce publications in top conferences and journals. To offer guidance and assistance to any students involved in the project
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 5 hours ago
.) Departmental Preferred Experience, Skills, Training/Education: Ph.D. in design-related discipline, specializing in deep learning and biometrics. A strong background with evidence of experimental and AI/machine
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results. Preferred Qualifications: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud
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skills and knowledge of computer programming such as Unix, Python and R Previous experience with multi-omics and their integration Deep technical understanding of bioinformatics analysis approaches and
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/rehabilitation applications. It is expected that the application has the knowledge of: 1) deep learning, large AI models, large language models; 2) the rendering techniques for generating human body animations
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research and learning. Incorporates advanced methodologies such as deep neural networks and language/multimodal models, leading the preparation of scientific Required Department Minimum Qualifications: Ph.D
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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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and managing scalable ETL (Extract, Transform, Load) pipelines to integrate multi-source, multimodal datasets Applying big-data analytic, including spatio-temporal modeling and deep learning techniques