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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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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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support for the lab Job Requirements: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in world model, computer vision and deep learning
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in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the above academic fields are encouraged to apply. High proficiency in
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, Machine Learning, or related areas. - Knowledge of Large Language Models and Retrieval-Augmented Generation. - Experience or interest in Knowledge Graphs, Semantic Web technologies, information retrieval
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paradigms. We are also leveraging several analytical tools such as computational modelling and machine learning. As a Research Assistant, you will help collect, analyse and interpret EEG data collected in
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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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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environment, working with an academic project team on robotics, machine learning and cyber-security. The project is supported by the University's research and innovation infrastructure and has a clear focus on