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Be You. At Duke, we celebrate individuality and the unique perspectives that each member of our community brings. As the Machine Learning Research Data Analyst, Ophthalmic Imaging, you'll play a
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to the activities of the Machine Learning & Data Science research group, develop rigorous mathematical proofs for convergence and asymptotic analysis of deep learning models, methods and algorithms, and collaborate
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Technologies. The researchers will develop intelligent agents that collaborate, verify recommendations and learn from experience to improve wireless network energy efficiency. The research combines large
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to achieve the project’s objectives, with a particular emphasis on efficient and safe learning approaches for collaborative multi-agent reinforcement learning tasks. You will contribute to advancing BOLD’s
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collection, data storage, algorithms, and AI learning principles that allow components of a distributed AI system to be trained collaboratively to dynamically better cooperate toward a unified production
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of NSF-funded research on speciation under alternating cycles of gene flow and isolation (funding is currently anticipated for two years). Supported by PI Ethan Linck and external collaborators
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students working on research related to the project. Promotes a safe, compliant, and collaborative learning environment within the research group. Actively participates in the preparation of required
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states, tissue organization, and biological programs, and connect these representations to mechanisms of human disease. Your work will sit at the intersection of machine learning, computational biology
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. Through outstanding patient care, groundbreaking research, and exceptional education, we are advancing the future of behavioral health and developmental science. In this role, you will collaborate with
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with