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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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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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technology, building physics, HVAC systems, computer science and control systems architecture, thereby advancing all disciplines involved. The project is a collaboration with Building Services Engineering
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theoretical tools from the perspective of physical chemistry. The position requires sound verbal and written communication skills in English. If Swedish is not your native language, Chalmers offers Swedish
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theoretical foundations to applied systems development. We provide high-quality education at Bachelor's, Master's, and graduate levels, offering over 120 courses each year. We have extensive national and
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superconducting circuits. This project focuses on modeling the quantum dynamics of these circuits at the Hamiltonian level and optimizing their control. You will develop theoretical models and optimization
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Autonomous cyber-physical systems (ACPS) have great potential to improve our ways of life, increasing mobility, cutting costs, and saving lives. Considering the complexity of the environments