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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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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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To fully benefit from automated vehicles (AVs), they must be both safe and appreciated by drivers. This post-doc is to use modeling (e.g., AI/machine learning) and behavior data to predict perceived
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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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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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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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the Master’s programmes in Biomedical Engineering, Systems, Control and Mechatronics, Communication Engineering, and Electrical Power Engineering. Learn more: www.chalmers.se/en/departments/e2 Main
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postdocs. Research areas include wireless communication, optical communication, coding theory, information theory, and machine learning. We value diversity and believe that a mix of backgrounds and
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Python and MATLAB Documented experience with analysis of complex scientific data e.g. through machine learning What you will do execute experimental tasks, such as planning of experiments alone or together