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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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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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related field. Background in physics-based battery modelling and/or machine learning is considered a strong merit. Excellent communication skills in English, both written and spoken. Contract terms Full
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-capacity wireless communication solutions. As part of a cross-disciplinary team, you will be expected to present your research results at project meetings, co-supervise PhD students, and contribute
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from automated vehicles (AVs), they must be both safe and appreciated by drivers. This project uses modeling (e.g., AI/machine learning) and human behavior data to predict perceived safety and quantify
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strong expertise in control theory, machine learning, and probability. You will also collaborate with: Vehicle Safety Division , which applies systems engineering and human factors to improve traffic
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technologies for medical diagnostics, treatment, and monitoring. Our research activities span computational modeling, algorithm development (using both traditional signal processing and machine learning
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML
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of geometric deep learning and add rigorous arguments to a debate driven by empirical results. Who we are looking for We seek candidates with the following qualifications: To qualify as a PhD student, you must
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PhD in electrical engineering (or related field), with specialization in radio localization, radar, sensing, signal processing, or machine learning Have completed your PhD no more than three years