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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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qualified individual with an MSc degree corresponding to at least 240 higher education credits in civil or mechanical engineering, applied physics, or a related discipline. The ideal candidate will possess
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importance, for triggering shallow landslides in sensitive clays. The focus will be on developing computational models that will quantify the mechanisms, precursors and the time to failure. This will be
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. Of particular interest is the investigation of compositional methods for constructing runtime monitors. The candidate will build on the latest advances in formal methods and learning theory, to develop methods
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industry have started exploring alternative materials, devices, and circuit architectures, particularly in the rapidly expanding field of AI hardware. Atomically thin two-dimensional transition metal
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The main purpose of this project is to develop new biobased surfactants using sustainable raw materials as an alternative to conventional petrochemical-derived surfactants. Since most surfactants
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of Industrial and Materials Science (IMS) . The Maintenance Engineering research group is a vibrant, ambitious, and multi-disciplinary team of international researchers driven by the vision to renew