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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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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Transformers are central to many of today’s most successful AI models, from language understanding to computer vision. Yet, their success remains largely empirical, with limited theoretical understanding
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Master’s degree in Applied Mechanics, Mechanical Engineering, or a closely related field. Strong knowledge of fluid mechanics, CFD, turbulence modelling, and structural mechanics. Understanding
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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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include fine-grained and bounded space cryptography, realization of idealized models, relationship between cryptographic notions, and similar topics in foundational cryptography. Exploring connections
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in a scientific programming language (e.g., Python or Julia). Strong mathematical and analytical skills. A collaborative mindset and enthusiasm for teamwork. Desired: Experience in modeling quantum
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of digital communications and signal processing. High grades in the core courses are required. Skills in mathematical analysis, modeling, and network algorithms are essential. Analytical thinking, eagerness