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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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At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend/Integrated PhD stipend in theories for programming semantics, methods for verification and reliability
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-based symbolic machine learning. The aim is to give the industrial widely used NewWave theory (a theory that predicts the linear shape of extreme waves) a modern twist to account for the important
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of polycrystalline metals. The post holder will be located in Central Cambridge, Cambridgeshire, UK. The key responsibilities and duties include setting up the framework; validating the results using theory and
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join the Operational Quantum Information Team (Dakić Group) to work on topics in quantum foundations and quantum information theory. Your personal sphere of influence: As a university assistant (praedoc
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based in the Physics Building at the Streatham Campus in Exeter. Quantum energetics (theory) This PhD position is concerned with the research fields of theoretical quantum optics, quantum technologies and
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media and artificial intelligence, are transforming the conditions for political communication, mobilisation, and t power in contemporary societies. The project should be grounded in sociological theory
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The DFG-funded Research Training Group (GRK 2717) „Dynamics of Controlled Atomic and Molecular Systems (DynCAM)“ at the University of Freiburg in collaboration with the University of British
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, and advancing quantum verification theory. Our team is part of the Quantum Optics, Quantum Nanophysics, and Quantum Information group of the Faculty of Physics. We are a member of the Vienna Center
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electrolyte and electrodes and their influence on the overall performance and safety of sodium-based batteries and their chemistry You cooperate with partners from theory to optimize charge transport