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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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to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how these forces affect the degradation and lifetime of the machines. About the position The position is based
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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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
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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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of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial intelligence in general. The focus is broadly upon the development
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for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing group in the Quantum
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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