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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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search is nuclear theory; a research area in which we have a strong international position and benefit from exceptional collaborations. Organizationally we are part of the Division of Subatomic, High
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, for example via equilibrium and non-equilibrium Green's functions, density functional theory, and tensor network methods. Experience in topological magnetic systems and nonlinear optical phenomena is considered
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supervised by Sebastian Throm. The subject area of the announced position covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together
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specific focus on Aerial and Space robotics. The vision of RAI is aiming in closing the gap from theory to real life, while the team has a strong expertise in field robotics. Specific application areas
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application! We are looking for a PhD student in theoretical physics with a focus on the theory of magnetic materials. Your work assignments Your tasks will be to carry out research using advanced theoretical
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methods and social theory (suitable subjects include but are not limited to human geography, anthropology, sociology, rural development, sustainability studies), completed latest by the 31st of August
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covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together with the successful candidate upon personal background and interests
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of computational solid-state materials research! As a PhD student in the Condensed Matter and Materials Theory division , you will have the opportunity to explore surface and device physics, using density functional
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. The project will develop fundamental theory and tools that will be key for understanding biological mechanisms causing diseases that are due to gene dysregulation, such as cancer. The core of the project is a