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into the School's activities. We are particularly interested in candidates with expertise in Digital Health and AI in Medicine, including machine learning (especially deep learning), natural language processing, and
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. The work will primarily entail design, implementation, and evaluation of distributed systems and networks for machine learning inference. Applying machine learning concepts, with the goal of devising agentic
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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Engineering, or related fields. They must have proven experience in the development and programming of Information Systems; prior experience in applications based on Data Analytics/Machine Learning and data
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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent
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into the School’s activities. We are particularly interested in candidates with expertise in Digital Health and AI in Medicine, including machine learning (especially deep learning), natural language processing, and
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retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary The Darla Moore School
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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; Visual Computing, Medical Informatics, and Bioinformatics; Web Intelligence and Recommender Systems; Computing Education; Interactive Machine Learning; Cybersecurity and Adversarial Machine Learning