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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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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
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applications. Project description This PhD project focuses on advancing the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning
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learning that can generalize. Duties The PhD student will carry out research in the area of distributed machine learning The PhD student will actively contribute to setting up the research questions in
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experts in the field of protein engineering, protein production, affinity ligand design and characterization, and machine learning for protein design. This unique PhD position is a 4-year collaborative
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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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cybersecurity team at Luleå University of Technology and also our partner within Cybercampus Sweden, RISE. We will use methods and technologies including expert systems, evolutionary algorithms, machine learning
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application! We are now looking for 1-2 PhD students in Automatic Control, at the Department of Electrical Engineering (ISY). Your work assignments The announced positions are within our new competence center
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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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, which is crucial for rutting, using machine learning. Second, we will develop new systems to integrate data from radar and lidar sensors mounted on drones and forestry machines to improve future real-time