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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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application! We are looking for a PhD student in Computer Science formally based at the Department of Computer and Information Science (IDA) as part of the national research program WASP. Wallenberg AI
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for Computer Vision conducts research and education in machine learning for computer vision at the undergraduate, advanced, and PhD levels. CVL has been identified as an outstanding Swedish research environment
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Description of the workplace The position will be based at the Division of Computer Vision and Machine Learning (CVML) at the Centre for Mathematical Sciences (https://www.maths.lu.se). CVML has
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data and clinical information. Applicants must hold (or be close to completing) a PhD in a relevant field and have expertise in modern computer vision and AI research. Experience with biomedical data
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for materials science, and advanced optimizers for modern deep learning. The research may be conducted in collaboration with the Electronic and Photonic Materials and/or the Computer Vision Laboratory
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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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build the sustainable companies and societies of the future. Our machine learning group is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP). WASP is Sweden’s largest individual
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome