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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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Engineering and Autonomous Systems division . We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and learning
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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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Merits: Experience with Matlab Prior coursework or project experience in railway mechanics Background in signal processing Knowledge of machine learning techniques Main responsibilities Your primary
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The applicant should have a PhD degree in transport, operations research, applied mathematics, computer science, or similar topics. Experience with optimization, data-driven or machine-learning skills
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models into Chalmers’ bridge simulators in collaboration with other researchers. You are also expected to supervise PhD and MSc students and to publish at least two peer-reviewed journal articles during
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-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning. Our department continuously strives to be an attractive employer. Equality
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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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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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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