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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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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control
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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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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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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