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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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, 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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focusing on: Quantum mechanical calculations using density functional theory. Mean-field modeling and Monte Carlo simulations for reaction kinetics. Theoretical spectroscopy By combining quantum mechanical
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(CFD), including chemical reaction models, but could also involve other modeling methods such as Density Functional Theory (DFT) or kinetic Monte Carlo. Your qualifications You have graduated at Master’s
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work together, cooperative autonomous systems emerge. These systems hold transformative potential in applications like robotics, environmental monitoring, smart transportation, warehouse automation, and
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to fields such as critical theory, digital sociology, criminology, or science and technology studies (STS). As part of the application, the doctoral student will draft a brief plan for an independent project
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third-cycle courses. The last day to apply is May 30, 2025. Project description and tasks: The PhD student will carry out research in the analysis of Partial Differential Equations as part of a project
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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and
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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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epidemiology, eating disorders, precision medicine, and biostatistics. Part of the success of our department is due to our collaborative spirit where one factor is that researchers at the Department share and co