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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Join us for a fully funded PhD position in theoretical machine learning to uncover how and why transformers work. Explore their inner mechanisms using information theory. As part of this project
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application! We are looking for up to two PhD students in Generative AI and Machine Learning Your work assignments We are looking for up to two PhD students working on generative AI/machine learning, with
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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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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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machine learning techniques into a modern AI planning system. The project will involve both theoretical and experimental work As a PhD student, you devote most of your time to doctoral studies and the
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug