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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
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reducing the overall carbon footprint and promoting ecological responsibility. As a PhD student, you will join our Machine Learning group in Sustainable Machine Learning. As part of our dynamic research
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applications. Project description This PhD project focuses on advancing the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning
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learning that can generalize. Duties The PhD student will carry out research in the area of distributed machine learning The PhD student will actively contribute to setting up the research questions in
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of Tübingen, University of Sydney, and Aalto University. We strive for all PhD students to get a solid international experience during their PhD. Project description Machine learning methods hold the potential
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: linear algebra, calculus, numerical linear algebra, optimization, statistical machine learning, deep learning, and software engineering. Rules governing PhD students are set out in the Higher Education
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project "Machine-learning enhanced modeling of complex crack networks in anisotropic rail and wheel materials.” This exciting opportunity brings together computational and experimental material mechanics
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: statistical machine learning, optimisation, linear algebra, calculus, deep learning, programming, dynamical systems and control. Rules governing PhD students are set out in the Higher Education Ordinance
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, Evolution, and Disease. We are looking for a PhD with a strong quantitative background and hands-on experience in either machine learning force field estimation or modern generative models, who is eager about
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs