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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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analysis, data organization and quality control, software testing, visualization of research data, scientific documentation, and general laboratory research support while working collaboratively with a
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using wearable electroencephalogram (EEG) during sleep. Our goal is to pioneer developments in wearable technology that integrate reinforcement learning to discover novel strategies for human interaction
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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faculty affiliated with the policy research taskforce of the Critical Minerals Hub at Duke University and will support collaboration and integration at the nexus of multiple disciplines involved in the Hub
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) Human-AI symbiosis, with the initial use case of aiding in neuromuscular rehabilitation. Researchers will collaborate with leading scholars in AI, machine learning, robotics, reinforcement learning, and
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, with the possibility of renewal, in the field of Analysis and Partial Differential Equations (PDEs). Job description - The successful candidate will collaborate on research projects related
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analysis, and data visualization. Apply advanced statistical, machine learning and AI methods when appropriate. Review analysis outputs and ensure methodological consistency and quality. Collaborate with
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to research projects involving LLMs and related AI systems, including collaborations with project partners, guidance to students, participation in and organisation of seminars and conferences, and development