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quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a welcoming and international work environment. The research group in
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Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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communications theory methods Applying optimization techniques and machine learning/AI approaches Conducting simulations and experimental validations Collaborating with Ericsson and Chalmers researchers Publishing
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning
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decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en/article/open-positions
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of extensive datasets. You will be supervised researchers who collectively offer expertise in computational biology, genetics, epidemiology, and machine learning. The research will be closely linked
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. Your profile You have documented expertise in marine ecology and computer vision and machine learning methods for video-based fish monitoring. You have excellent IT skills and experience in handling
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student in Statistics who can perform high quality statistical research. Apply January 6, 2026, at the latest. We are seeking a PhD student within the WASP-HS project “Machine learning to study causality